Maciej A Mazurowski

Journal Article Abdom Radiol (NY) · October 2024 Accurate, automated MRI series identification is important for many applications, including display ("hanging") protocols, machine learning, and radiomics. The use of the series description or a pixel-based classifier each has limitations. We demonstrate a ... Full text Link to item Cite

A publicly available deep learning model and dataset for segmentation of breast, fibroglandular tissue, and vessels in breast MRI.

Journal Article Sci Rep · March 5, 2024 Breast density, or the amount of fibroglandular tissue (FGT) relative to the overall breast volume, increases the risk of developing breast cancer. Although previous studies have utilized deep learning to assess breast density, the limited public availabil ... Full text Link to item Cite

Computed Tomography Volumetrics for Size Matching in Lung Transplantation for Restrictive Disease.

Journal Article Ann Thorac Surg · February 2024 BACKGROUND: There is no consensus on the optimal allograft sizing strategy for lung transplantation in restrictive lung disease. Current methods that are based on predicted total lung capacity (pTLC) ratios do not account for the diminutive recipient chest ... Full text Link to item Cite

Convolutional neural networks rarely learn shape for semantic segmentation

Journal Article Pattern Recognition · February 1, 2024 Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes. While some research on the topic is emerging, there is no systematic study to conc ... Full text Cite

Simplifying risk stratification for thyroid nodules on ultrasound: validation and performance of an artificial intelligence thyroid imaging reporting and data system.

Journal Article Curr Probl Diagn Radiol · 2024 PURPOSE: To validate the performance of a recently created risk stratification system (RSS) for thyroid nodules on ultrasound, the Artificial Intelligence Thyroid Imaging Reporting and Data System (AI TI-RADS). MATERIALS AND METHODS: 378 thyroid nodules fr ... Full text Link to item Cite

THE EFFECT OF INTRINSIC DATASET PROPERTIES ON GENERALIZATION: UNRAVELING LEARNING DIFFERENCES BETWEEN NATURAL AND MEDICAL IMAGES

Conference 12th International Conference on Learning Representations, ICLR 2024 · January 1, 2024 This paper investigates discrepancies in how neural networks learn from different imaging domains, which are commonly overlooked when adopting computer vision techniques from the domain of natural images to other specialized domains such as medical images. ... Cite

Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach.

Journal Article J Digit Imaging · December 2023 Large numbers of radiographic images are available in musculoskeletal radiology practices which could be used for training of deep learning models for diagnosis of knee abnormalities. However, those images do not typically contain readily available labels ... Full text Link to item Cite

SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images.

Journal Article IEEE Trans Med Imaging · December 2023 Anomaly detection (AD) aims to determine if an instance has properties different from those seen in normal cases. The success of this technique depends on how well a neural network learns from normal instances. We observe that the learning difficulty scale ... Full text Link to item Cite

Segment anything model for medical image analysis: An experimental study.

Journal Article Med Image Anal · October 2023 Training segmentation models for medical images continues to be challenging due to the limited availability of data annotations. Segment Anything Model (SAM) is a foundation model trained on over 1 billion annotations, predominantly for natural images, tha ... Full text Link to item Cite

MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.

Journal Article Radiology · October 2023 Background PET can be used for amyloid-tau-neurodegeneration (ATN) classification in Alzheimer disease, but incurs considerable cost and exposure to ionizing radiation. MRI currently has limited use in characterizing ATN status. Deep learning techniques ca ... Full text Link to item Cite

Duke Liver Dataset: A Publicly Available Liver MRI Dataset with Liver Segmentation Masks and Series Labels.

Journal Article Radiol Artif Intell · September 2023 The Duke Liver Dataset contains 2146 abdominal MRI series from 105 patients, including a majority with cirrhotic features, and 310 image series with corresponding manually segmented liver masks. ... Full text Link to item Cite

Feasibility of predicting a screening digital breast tomosynthesis recall using features extracted from the electronic medical record.

Journal Article Eur J Radiol · September 2023 PURPOSE: Tools to predict a screening mammogram recall at the time of scheduling could improve patient care. We extracted patient demographic and breast care history information within the electronic medical record (EMR) for women undergoing digital breast ... Full text Link to item Cite

Deep learning for classification of thyroid nodules on ultrasound: validation on an independent dataset.

Journal Article Clin Imaging · July 2023 OBJECTIVES: The purpose is to apply a previously validated deep learning algorithm to a new thyroid nodule ultrasound image dataset and compare its performances with radiologists. METHODS: Prior study presented an algorithm which is able to detect thyroid ... Full text Link to item Cite

Multistep Automated Data Labelling Procedure (MADLaP) for thyroid nodules on ultrasound: An artificial intelligence approach for automating image annotation.

Journal Article Artif Intell Med · July 2023 Machine learning (ML) for diagnosis of thyroid nodules on ultrasound is an active area of research. However, ML tools require large, well-labeled datasets, the curation of which is time-consuming and labor-intensive. The purpose of our study was to develop ... Full text Link to item Cite

Unsupervised anomaly localization in high-resolution breast scans using deep pluralistic image completion.

Journal Article Med Image Anal · July 2023 Automated tumor detection in Digital Breast Tomosynthesis (DBT) is a difficult task due to natural tumor rarity, breast tissue variability, and high resolution. Given the scarcity of abnormal images and the abundance of normal images for this problem, an a ... Full text Link to item Cite

Deep Learning for Breast MRI Style Transfer with Limited Training Data.

Journal Article J Digit Imaging · April 2023 In this work we introduce a novel medical image style transfer method, StyleMapper, that can transfer medical scans to an unseen style with access to limited training data. This is made possible by training our model on unlimited possibilities of simulated ... Full text Link to item Cite

Thyroid Nodules on Ultrasound in Children and Young Adults: Comparison of Diagnostic Performance of Radiologists' Impressions, ACR TI-RADS, and a Deep Learning Algorithm.

Journal Article AJR Am J Roentgenol · March 2023 BACKGROUND. In current clinical practice, thyroid nodules in children are generally evaluated on the basis of radiologists' overall impressions of ultrasound images. OBJECTIVE. The purpose of this article is to compare the diagnostic performance of radiolo ... Full text Link to item Cite

A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis.

Journal Article JAMA network open · February 2023 ImportanceAn accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide.ObjectivesTo make tra ... Full text Cite

SuperMask: Generating High-resolution object masks from multi-view, unaligned low-resolution MRIs

Conference Proceedings of Machine Learning Research · January 1, 2023 Three-dimensional segmentation in magnetic resonance images (MRI), which reflects the true shape of the objects, is challenging since high-resolution isotropic MRIs are rare and typical MRIs are anisotropic, with the out-of-plane dimension having a much lo ... Cite

Reverse Engineering Breast MRIs: Predicting Acquisition Parameters Directly from Images

Conference Proceedings of Machine Learning Research · January 1, 2023 The image acquisition parameters (IAPs) used to create MRI scans are central to defining the appearance of the images. Deep learning models trained on data acquired using certain parameters might not generalize well to images acquired with different parame ... Cite

The Need for Targeted Labeling of Machine Learning-Based Software as a Medical Device.

Journal Article JAMA Netw Open · November 1, 2022 Full text Link to item Cite

Artificial Intelligence (AI) Tools for Thyroid Nodules on Ultrasound, From the AJR Special Series on AI Applications.

Journal Article AJR Am J Roentgenol · October 2022 Artificial intelligence (AI) methods for evaluating thyroid nodules on ultrasound have been widely described in the literature, with reported performance of AI tools matching or in some instances surpassing radiologists' performance. As these data have acc ... Full text Link to item Cite

Anomaly Detection of Calcifications in Mammography Based on 11,000 Negative Cases.

Journal Article IEEE Trans Biomed Eng · May 2022 In mammography, calcifications are one of the most common signs of breast cancer. Detection of such lesions is an active area of research for computer-aided diagnosis and machine learning algorithms. Due to limited numbers of positive cases, many supervise ... Full text Link to item Cite

Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.

Journal Article BMC Med Inform Decis Mak · April 15, 2022 BACKGROUND: There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed t ... Full text Open Access Link to item Cite

3D Pyramid Pooling Network for Abdominal MRI Series Classification.

Journal Article IEEE Trans Pattern Anal Mach Intell · April 2022 Recognizing and organizing different series in an MRI examination is important both for clinical review and research, but it is poorly addressed by the current generation of picture archiving and communication systems (PACSs) and post-processing workstatio ... Full text Link to item Cite

Prediction of Upstaging in Ductal Carcinoma in Situ Based on Mammographic Radiomic Features.

Journal Article Radiology · April 2022 Background Improving diagnosis of ductal carcinoma in situ (DCIS) before surgery is important in choosing optimal patient management strategies. However, patients may harbor occult invasive disease not detected until definitive surgery. Purpose To assess t ... Full text Link to item Cite

Classification of Multiple Diseases on Body CT Scans Using Weakly Supervised Deep Learning.

Journal Article Radiol Artif Intell · January 2022 PURPOSE: To design multidisease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. MATERIALS AND METHODS: This retrospective study included a total of 12 092 patients (mean age, ... Full text Link to item Cite

Quality or quantity: toward a unified approach for multi-organ segmentation in body CT

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2022 Organ segmentation of medical images is a key step in virtual imaging trials. However, organ segmentation datasets are limited in in terms of quality (because labels cover only a few organs) and quantity (since case numbers are limited). In this study, we ... Full text Cite

Virtual versus reality: external validation of COVID-19 classifiers using XCAT phantoms for chest radiography

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2022 Many published studies use deep learning models to predict COVID-19 from chest x-ray (CXR) images, often reporting high performances. However, the models do not generalize well on independent external testing. Common limitations include the lack of medical ... Full text Cite

Virtual vs. reality: External validation of COVID-19 classifiers using XCAT phantoms for chest computed tomography

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2022 Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtual imag ... Full text Cite

The Intrinsic Manifolds of Radiological Images and Their Role in Deep Learning

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2022 The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural networks learn from the data. Intrinsic dataset manifolds and their relations ... Full text Cite

Lightweight Transformer Backbone for Medical Object Detection

Conference Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2022 Lesion detection in digital breast tomosynthesis (DBT) is an important and a challenging problem characterized by a low prevalence of images containing tumors. Due to the label scarcity problem, large deep learning models and computationally intensive algo ... Full text Cite

Normalization of breast MRIs using cycle-consistent generative adversarial networks.

Journal Article Computer methods and programs in biomedicine · September 2021 ObjectivesDynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI) is widely used to complement ultrasound examinations and x-ray mammography for early detection and diagnosis of breast cancer. However, images generated by various MRI scanne ... Full text Cite

A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images.

Journal Article JAMA Netw Open · August 2, 2021 IMPORTANCE: Breast cancer screening is among the most common radiological tasks, with more than 39 million examinations performed each year. While it has been among the most studied medical imaging applications of artificial intelligence, the development a ... Full text Link to item Cite

Do We Expect More from Radiology AI than from Radiologists?

Journal Article Radiol Artif Intell · July 2021 The expectations of radiology artificial intelligence do not match expectations of radiologists in terms of performance and explainability. ... Full text Link to item Cite

Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists.

Journal Article Comput Biol Med · June 2021 UNLABELLED: A fully-automated deep learning algorithm matched performance of radiologists in assessment of knee osteoarthritis severity in radiographs using the Kellgren-Lawrence grading system. PURPOSE: To develop an automated deep learning-based algorith ... Full text Link to item Cite

A generative adversarial network-based abnormality detection using only normal images for model training with application to digital breast tomosynthesis.

Journal Article Sci Rep · May 13, 2021 Deep learning has shown tremendous potential in the task of object detection in images. However, a common challenge with this task is when only a limited number of images containing the object of interest are available. This is a particular issue in cancer ... Full text Link to item Cite

Machine-learning-based multiple abnormality prediction with large-scale chest computed tomography volumes.

Journal Article Med Image Anal · January 2021 Machine learning models for radiology benefit from large-scale data sets with high quality labels for abnormalities. We curated and analyzed a chest computed tomography (CT) data set of 36,316 volumes from 19,993 unique patients. This is the largest multip ... Full text Link to item Cite

Deep neural networks trained for segmentation are sensitive to brightness changes: Preliminary results

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2021 Medical images of a patient may have a significantly different appearance depending on imaging modality (e.g. MRI vs. CT), sequence type (e.g., T1-weighted MRI vs. T2-weighted MRI), and even manufacturer/model of equipment used for the same modality and se ... Full text Cite

Performance of preoperative breast MRI based on breast cancer molecular subtype.

Journal Article Clin Imaging · November 2020 PURPOSE: To assess the performance of preoperative breast MRI biopsy recommendations based on breast cancer molecular subtype. METHODS: All preoperative breast MRIs at a single academic medical center from May 2010 to March 2014 were identified. Reports we ... Full text Link to item Cite

Using the American College of Radiology Thyroid Imaging Reporting and Data System at the Point of Care: Sonographer Performance and Interobserver Variability.

Journal Article Ultrasound Med Biol · August 2020 The purpose of this study was to assess inter-observer variability and performance when sonographers assign features to thyroid nodules on ultrasound using the American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TI-RADS). Fifteen ... Full text Link to item Cite

Prediction of Upstaged Ductal Carcinoma In Situ Using Forced Labeling and Domain Adaptation.

Journal Article IEEE Trans Biomed Eng · June 2020 OBJECTIVE: The goal of this study is to use adjunctive classes to improve a predictive model whose performance is limited by the common problems of small numbers of primary cases, high feature dimensionality, and poor class separability. Specifically, our ... Full text Link to item Cite

Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images.

Journal Article Ultrasound Med Biol · February 2020 Computer-aided segmentation of thyroid nodules in ultrasound imaging could assist in their accurate characterization. In this study, using data for 1278 nodules, we proposed and evaluated two methods for deep learning-based segmentation of thyroid nodules ... Full text Link to item Cite

Breast Cancer Radiogenomics: Current Status and Future Directions.

Journal Article Acad Radiol · January 2020 Radiogenomics is an area of research that aims to identify associations between imaging phenotypes ("radio-") and tumor genome ("-genomics"). Breast cancer radiogenomics research in particular has been an especially prolific area of investigation in recent ... Full text Link to item Cite

Artificial Intelligence in Radiology: Some Ethical Considerations for Radiologists and Algorithm Developers.

Journal Article Acad Radiol · January 2020 As artificial intelligence (AI) is finding its place in radiology, it is important to consider how to guide the research and clinical implementation in a way that will be most beneficial to patients. Although there are multiple aspects of this issue, I con ... Full text Link to item Cite

Generative adversarial network-based image completion to identify abnormal locations in digital breast tomosynthesis images

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 Deep learning has achieved great success in image analysis and decision making in radiology. However, a large amount of annotated imaging data is needed to construct well-performing deep learning models. A particular challenge in the context of breast canc ... Full text Cite

A multitask deep learning method in simultaneously predicting occult invasive disease in ductal carcinoma in-situ and segmenting microcalcifications in mammography

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 We proposed a two-branch multitask learning convolutional neural network to solve two different but related tasks at the same time. Our main task is to predict occult invasive disease in biopsy proven Ductal Carcinoma in-situ (DCIS), with an auxiliary task ... Full text Cite

Weakly supervised 3D classification of chest CT using aggregated multi-resolution deep segmentation features

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations, where even a positive scan can hold hundreds to thousands of negative slices along multiple planes. Furthermore, although deep learning s ... Full text Cite

MRI image harmonization using cycle-consistent generative adversarial network

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a valuable modality for evaluating breast abnormalities found in mammography and performing early disease detection in high-risk patients. However, images produced by various MRI scanners (e ... Full text Cite

Automatic Kellgren-Lawrence grade estimation driven deep learning algorithms

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 Knee osteoarthritis (OA) is a prevalent and disabling degenerative joint disease. Objectively identifying knee OA severity is challenging given significant inter-reader variability due to human interpretation factors. The Kellgren-Lawrence (KL) grading sys ... Full text Cite

Automatic estimation of knee joint space narrowing by deep learning segmentation algorithms

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 Evaluating the severity of knee osteoarthritis (OA) accounts for significant plain film workload and is a crucial component of knee radiograph interpretation, which informs surgical decision-making for costly and invasive procedures such as knee replacemen ... Full text Cite

Virtual imaging trials: An emerging experimental paradigm in imaging research and practice

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2020 As medical imaging technologies continue to accelerate in complexity, application, and multiplicity of design choices and use features, they should ideally be evaluated and optimized through human clinical trials. However, such trials are often impossible ... Full text Cite

Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images.

Journal Article Radiology. Artificial intelligence · January 2020 PurposeTo employ deep learning to predict genomic subtypes of lower-grade glioma (LLG) tumors based on their appearance at MRI.Materials and methodsImaging data from The Cancer Imaging Archive and genomic data from The Cancer Genome Atlas ... Full text Cite

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ.

Journal Article Comput Biol Med · December 2019 PURPOSE: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the diagnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. MATERIALS AND METHODS: Our study ... Full text Link to item Cite

Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists.

Journal Article Radiology · September 2019 BackgroundManagement of thyroid nodules may be inconsistent between different observers and time consuming for radiologists. An artificial intelligence system that uses deep learning may improve radiology workflow for management of thyroid nodules.PurposeT ... Full text Link to item Cite

Machine learning-based prediction of future breast cancer using algorithmically measured background parenchymal enhancement on high-risk screening MRI.

Journal Article J Magn Reson Imaging · August 2019 BACKGROUND: Preliminary work has demonstrated that background parenchymal enhancement (BPE) assessed by radiologists is predictive of future breast cancer in women undergoing high-risk screening MRI. Algorithmically assessed measures of BPE offer a more pr ... Full text Link to item Cite

Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce.

Journal Article J Am Coll Radiol · August 2019 The increasingly realistic prospect of artificial intelligence (AI) playing an important role in radiology has been welcomed with a mixture of enthusiasm and anxiousness. A consensus has arisen that AI will support radiologists in the interpretation of les ... Full text Link to item Cite

Using Artificial Intelligence to Revise ACR TI-RADS Risk Stratification of Thyroid Nodules: Diagnostic Accuracy and Utility.

Journal Article Radiology · July 2019 Background Risk stratification systems for thyroid nodules are often complicated and affected by low specificity. Continual improvement of these systems is necessary to reduce the number of unnecessary thyroid biopsies. Purpose To use artificial intelligen ... Full text Link to item Cite

Deep learning for identifying radiogenomic associations in breast cancer.

Journal Article Comput Biol Med · June 2019 RATIONALE AND OBJECTIVES: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: In this institutional review board- ... Full text Link to item Cite

Association of distant recurrence-free survival with algorithmically extracted MRI characteristics in breast cancer.

Journal Article J Magn Reson Imaging · June 2019 BACKGROUND: While important in diagnosis of breast cancer, the scientific assessment of the role of imaging in prognosis of outcomes and treatment planning is limited. PURPOSE: To evaluate the potential of using quantitative imaging variables for stratifyi ... Full text Link to item Cite

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm.

Journal Article Comput Biol Med · June 2019 Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and ... Full text Link to item Cite

Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Journal Article J Magn Reson Imaging · April 2019 Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety of sop ... Full text Link to item Cite

How accurate and precise are CT based measurements of iodine concentration? A comparison of the minimum detectable concentration difference among single source and dual source dual energy CT in a phantom study.

Journal Article Eur Radiol · April 2019 OBJECTIVES: To assess the impact of scan- and patient-related factors on the error and the minimum detectable difference in iodine concentration among different generations of single-source (SS) fast kV-switching and dual-source (DS) dual-energy CT (DECT). ... Full text Link to item Cite

Can Texture Analysis Be Used to Distinguish Benign From Malignant Adrenal Nodules on Unenhanced CT, Contrast-Enhanced CT, or In-Phase and Opposed-Phase MRI?

Journal Article AJR Am J Roentgenol · March 2019 OBJECTIVE: The purpose of this study is to determine whether second-order texture analysis can be used to distinguish lipid-poor adenomas from malignant adrenal nodules on unenhanced CT, contrast-enhanced CT (CECT), and chemical-shift MRI. MATERIALS AND ME ... Full text Link to item Cite

Hierarchical Convolutional Neural Networks for Segmentation of Breast Tumors in MRI With Application to Radiogenomics.

Journal Article IEEE Trans Med Imaging · February 2019 Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance probl ... Full text Link to item Cite

Relationship between Background Parenchymal Enhancement on High-risk Screening MRI and Future Breast Cancer Risk.

Journal Article Acad Radiol · January 2019 RATIONALE AND OBJECTIVES: To determine if background parenchymal enhancement (BPE) on screening breast magnetic resonance imaging (MRI) in high-risk women correlates with future cancer. MATERIALS AND METHODS: All screening breast MRIs (n = 1039) in high-ri ... Full text Link to item Cite

Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set.

Journal Article Breast Cancer Res Treat · January 2019 PURPOSE: To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict pathologic complete response (pCR) to neoadjuvant therap ... Full text Link to item Cite

Malignant microcalcification clusters detection using unsupervised deep autoencoders

Journal Article Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2019 Detection and localization of microcalcification (MC) clusters are very important in mammography diagnosis. Supervised MC detectors require learning from extracted individual MCs and MC clusters. However, they are limited by number of datasets given that M ... Full text Cite

Combining deep learning methods and human knowledge to identify abnormalities in computed tomography (CT) reports

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2019 Many researchers in the field of machine learning have addressed the problem of detecting anomalies within Computed Tomography (CT) scans. Training these machine learning algorithms requires a dataset of CT scans with identified anomalies (labels), usually ... Full text Cite

Deep learning of 3D computed tomography (CT) images for organ segmentation using 2D multi-channel SegNet model

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2019 Purpose To accurately segment organs from 3D CT image volumes using a 2D, multi-channel SegNet model consisting of a deep Convolutional Neural Network (CNN) encoder-decoder architecture. Method We trained a SegNet model on the extended cardiac-Torso (XCAT) ... Full text Cite

Intra-tumor molecular heterogeneity in breast cancer: definitions of measures and association with distant recurrence-free survival.

Journal Article Breast Cancer Res Treat · November 2018 PURPOSE: The purpose of the study was to define quantitative measures of intra-tumor heterogeneity in breast cancer based on histopathology data gathered from multiple samples on individual patients and determine their association with distant recurrence-f ... Full text Link to item Cite

A systematic study of the class imbalance problem in convolutional neural networks.

Journal Article Neural Netw · October 2018 In this study, we systematically investigate the impact of class imbalance on classification performance of convolutional neural networks (CNNs) and compare frequently used methods to address the issue. Class imbalance is a common problem that has been com ... Full text Link to item Cite

Abdominal Radiography With Digital Tomosynthesis: An Alternative to Computed Tomography for Identification of Urinary Calculi?

Journal Article Urology · October 2018 OBJECTIVE: To compare the accuracy of plain abdominal radiography (kidneys, ureter, and bladder [KUB]) with digital tomosynthesis (DT) to noncontrast computed tomography (NCCT), the gold standard imaging modality for urinary stones. Due to radiation and co ... Full text Link to item Cite

Splenic contraction: a new member of the hypovolemic shock complex.

Journal Article Abdom Radiol (NY) · September 2018 OBJECTIVE: The objective of the article is to assess changes in splenic volume in the setting of hypovolemic shock; splenic enhancement in hypovolemic shock is also assessed. MATERIALS/METHODS: 71 consecutive adult patients with the hypovolemic shock compl ... Full text Link to item Cite

A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features.

Journal Article Br J Cancer · August 2018 BACKGROUND: Recent studies showed preliminary data on associations of MRI-based imaging phenotypes of breast tumours with breast cancer molecular, genomic, and related characteristics. In this study, we present a comprehensive analysis of this relationship ... Full text Link to item Cite

Breast cancer MRI radiomics: An overview of algorithmic features and impact of inter-reader variability in annotating tumors.

Journal Article Med Phys · July 2018 PURPOSE: To review features used in MRI radiomics of breast cancer and study the inter-reader stability of the features. METHODS: We implemented 529 algorithmic features that can be extracted from tumor and fibroglandular tissue (FGT) in breast MRIs. The f ... Full text Link to item Cite

Comparison of Visualization Rates of LI-RADS Version 2014 Major Features With IV Gadobenate Dimeglumine or Gadoxetate Disodium in Patients at Risk for Hepatocellular Carcinoma.

Journal Article AJR Am J Roentgenol · June 2018 OBJECTIVE: The purpose of this study is to compare visualization rates of the major features covered by Liver Imaging Reporting and Data System (LI-RADS) version 2014 in patients at risk for hepatocellular carcinoma using either gadobenate dimeglumine or g ... Full text Link to item Cite

A study of association of Oncotype DX recurrence score with DCE-MRI characteristics using multivariate machine learning models.

Journal Article J Cancer Res Clin Oncol · May 2018 PURPOSE: To determine whether multivariate machine learning models of algorithmically assessed magnetic resonance imaging (MRI) features from breast cancer patients are associated with Oncotype DX (ODX) test recurrence scores. METHODS: A set of 261 female ... Full text Link to item Cite

Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.

Journal Article Med Phys · March 2018 BACKGROUND AND PURPOSE: Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs. METHODS: We selected 44 glioblastoma (GBM) patie ... Full text Link to item Cite

Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features.

Journal Article J Am Coll Radiol · March 2018 PURPOSE: The aim of this study was to determine whether deep features extracted from digital mammograms using a pretrained deep convolutional neural network are prognostic of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core ... Full text Link to item Cite

Convolutional encoder-decoder for breast mass segmentation in digital breast tomosynthesis

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Digital breast tomosynthesis (DBT) is a relatively new modality for breast imaging that can provide detailed assessment of dense tissue within the breast. In the domains of cancer diagnosis, radiogenomics, and resident education, it is important to accurat ... Full text Cite

Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: A large scale evaluation

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 One of the methods widely used to measure the proliferative activity of cells in breast cancer patients is the immunohistochemical (IHC) measurement of the percentage of cells stained for nuclear antigen Ki-67. Use of Ki-67 expression as a prognostic marke ... Full text Cite

Breast mass detection in mammography and tomosynthesis via fully convolutional network-based heatmap regression

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Breast mass detection in mammography and digital breast tomosynthesis (DBT) is an essential step in computerized breast cancer analysis. Deep learning-based methods incorporate feature extraction and model learning into a unified framework and have achieve ... Full text Cite

Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ... Full text Cite

Deep learning-based features of breast MRI for prediction of occult invasive disease following a diagnosis of ductal carcinoma in situ: Preliminary data

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Approximately 25% of patients with ductal carcinoma in situ (DCIS) diagnosed from core needle biopsy are subsequently upstaged to invasive cancer at surgical excision. Identifying patients with occult invasive disease is important as it changes treatment a ... Full text Cite

Breast cancer molecular subtype classification using deep features: Preliminary results

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris-tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of t ... Full text Cite

Breast tumor segmentation in DCE-MRI using fully convolutional networks with an application in radiogenomics

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) remains an active as well as a challenging problem. Previous studies often rely on manual annotation for tumor regions, which is not only time-consuming but a ... Full text Cite

Improving classification with forced labeling of other related classes: Application to prediction of upstaged ductal carcinoma in situ using mammographic features

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2018 Predicting whether ductal carcinoma in situ (DCIS) identified at core biopsy contains occult invasive disease is an import task since these "upstaged" cases will affect further treatment planning. Therefore, a prediction model that better classifies pure D ... Full text Cite

Perception and Training

Chapter · January 1, 2018 Full text Cite

Effects of MRI scanner parameters on breast cancer radiomics.

Journal Article Expert Syst Appl · November 30, 2017 PURPOSE: To assess the impact of varying magnetic resonance imaging (MRI) scanner parameters on the extraction of algorithmic features in breast MRI radiomics studies. METHODS: In this retrospective study, breast imaging data for 272 patients were analyzed ... Full text Link to item Cite

Can algorithmically assessed MRI features predict which patients with a preoperative diagnosis of ductal carcinoma in situ are upstaged to invasive breast cancer?

Journal Article J Magn Reson Imaging · November 2017 PURPOSE: To assess the ability of algorithmically assessed magnetic resonance imaging (MRI) features to predict the likelihood of upstaging to invasive cancer in newly diagnosed ductal carcinoma in situ (DCIS). MATERIALS AND METHODS: We identified 131 pati ... Full text Link to item Cite

Relationships Between MRI Breast Imaging-Reporting and Data System (BI-RADS) Lexicon Descriptors and Breast Cancer Molecular Subtypes: Internal Enhancement is Associated with Luminal B Subtype.

Journal Article Breast J · September 2017 The aim of this study was to determine the associations between breast MRI findings using the Breast Imaging-Reporting and Data System (BI-RADS) lexicon descriptors and breast cancer molecular subtypes. In this retrospective, IRB-approved, single instituti ... Full text Link to item Cite

Structured reporting of CT enterography for inflammatory bowel disease: effect on key feature reporting, accuracy across training levels, and subjective assessment of disease by referring physicians.

Journal Article Abdom Radiol (NY) · September 2017 PURPOSE: To compare the content and accuracy of structured reporting (SR) versus non-structured reporting (NSR) for computed tomographic enterography (CTE) of inflammatory bowel disease (IBD). MATERIALS AND METHODS: This IRB-approved, HIPAA-compliant, retr ... Full text Link to item Cite

Can Occult Invasive Disease in Ductal Carcinoma In Situ Be Predicted Using Computer-extracted Mammographic Features?

Journal Article Acad Radiol · September 2017 RATIONALE AND OBJECTIVES: This study aimed to determine whether mammographic features assessed by radiologists and using computer algorithms are prognostic of occult invasive disease for patients showing ductal carcinoma in situ (DCIS) only in core biopsy. ... Full text Link to item Cite

Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data.

Journal Article J Neurooncol · May 2017 Recent studies identified distinct genomic subtypes of lower-grade gliomas that could potentially be used to guide patient treatment. This study aims to determine whether there is an association between genomics of lower-grade glioma tumors and patient out ... Full text Link to item Cite

Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study.

Journal Article J Neurooncol · March 2017 In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and ... Full text Link to item Cite

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer Disease: Do Regions Outside the Hippocampus Matter?

Journal Article AJNR Am J Neuroradiol · March 2017 BACKGROUND AND PURPOSE: Alzheimer disease is a prevalent neurodegenerative disease. Computer assessment of brain atrophy patterns can help predict conversion to Alzheimer disease. Our aim was to assess the prognostic efficacy of individual-versus-combined ... Full text Link to item Cite

Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?

Conference Proceedings of SPIE - The International Society for Optical Engineering · February 3, 2017 Cite

Algorithms for prediction of the Oncotype DX recurrence score using clinicopathologic data: a review and comparison using an independent dataset.

Journal Article Breast Cancer Res Treat · February 2017 PURPOSE: Given the potential savings in cost and resource utilization, several algorithms have been proposed to predict Oncotype DX recurrence score (ODX RS) using commonly acquired histopathologic variables. Although it is promising, additional independen ... Full text Link to item Cite

Radiogenomic analysis of lower grade glioma: A pilot multi-institutional study shows an association between quantitative image features and tumor genomics

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2017 Recent studies showed that genomic analysis of lower grade gliomas can be very effective for stratification of patients into groups with different prognosis and proposed specific genomic classifications. In this study, we explore the association of one of ... Full text Cite

Prediction of occult invasive disease in ductal carcinoma in situ using computer-extracted mammographic features

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2017 Predicting the risk of occult invasive disease in ductal carcinoma in situ (DCIS) is an important task to help address the overdiagnosis and overtreatment problems associated with breast cancer. In this work, we investigated the feasibility of using comput ... Full text Cite

Can upstaging of ductal carcinoma in situ be predicted at biopsy by histologic and mammographic features?

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2017 Reducing the overdiagnosis and overtreatment associated with ductal carcinoma in situ (DCIS) requires accurate prediction of the invasive potential at cancer screening. In this work, we investigated the utility of pre-operative histologic and mammographic ... Full text Cite

Can BI-RADS features on mammography be used as a surrogate for expensive genomic testing in breast cancer patients?

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2017 Medical oncologists increasingly rely on expensive genomic analysis to stratify patients for different treatment. The genomic markers are able to divide patients into groups that behave differently in terms of tumor presentation, likelihood of metastatic s ... Full text Cite

Deep learning for segmentation of brain tumors: Can we train with images from different institutions?

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2017 Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. Ho ... Full text Cite

Statistical aspects of radiogenomics: Can radiogenomics models be used to aid prediction of outcomes in cancer patients?

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2017 Radiogenomics is a new direction in cancer research that aims at identifying the relationship between tumor genomics and its appearance in imaging (i.e. its radiophenotype). Recent years brought multiple radiogenomic discoveries in brain, breast, lung, and ... Full text Cite

A computer vision-based algorithm to predict false positive errors in radiology trainees when interpreting digital breast tomosynthesis cases

Journal Article Expert Systems with Applications · December 1, 2016 Objectives Digital breast tomosynthesis (DBT) is a new imaging modality that improves invasive cancer detection rates compared to mammography. In this work, we aim to advance adaptive computer-based education in DBT by computer algorithm. Methods First, a ... Full text Cite

Predicting false negative errors in digital breast tomosynthesis among radiology trainees using a computer vision-based approach

Journal Article Expert Systems with Applications · September 1, 2016 Purpose Digital breast tomosynthesis (DBT) can improve lesion visibility in comparison to mammography by eliminating breast tissue superimposition. While the benefits of DBT in breast cancer screening rely on well trained radiologists, the optimal training ... Full text Cite

Interobserver variability in identification of breast tumors in MRI and its implications for prognostic biomarkers and radiogenomics.

Journal Article Med Phys · August 2016 PURPOSE: To assess the interobserver variability of readers when outlining breast tumors in MRI, study the reasons behind the variability, and quantify the effect of the variability on algorithmic imaging features extracted from breast MRI. METHODS: Four r ... Full text Link to item Cite

Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns.

Journal Article J Am Coll Radiol · February 2016 PURPOSE: The aim of this study was to better understand the relationship between digital breast tomosynthesis (DBT) difficulty and radiology trainee performance. METHODS: Twenty-seven radiology residents and fellows and three expert breast imagers reviewed ... Full text Link to item Cite

Author's Reply.

Journal Article J Am Coll Radiol · February 2016 Full text Link to item Cite

Identification of error making patterns in lesion detection on digital breast tomosynthesis using computer-extracted image features

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2016 Digital breast tomosynthesis (DBT) can improve lesion visibility by eliminating the issue of overlapping breast tissue present in mammography. However, this new modality likely requires new approaches to training. The issue of training in DBT is not well e ... Full text Cite

Radiogenomics of glioblastoma: A pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2016 Genomic subtype has been shown to be an important predictor of therapy response for patients with glioblastomas. Unfortunately, obtaining the genomic subtype is an expensive process that is not typically included in the standard of care. It is therefore of ... Full text Cite

Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape - Preliminary data

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2016 Glioblastoma (GBM) is the most common primary brain tumor characterized by very poor survival. However, while some patients survive only a few months, some might live for multiple years. Accurate prognosis of survival and stratification of patients allows ... Full text Cite

Recurrence-free survival in breast cancer is associated with MRI tumor enhancement dynamics quantified using computer algorithms.

Journal Article Eur J Radiol · November 2015 PURPOSE: The purpose of this study is to investigate the association between breast cancer recurrence-free survival and breast magnetic resonance imaging (MRI) tumor enhancement dynamics which are quantified semi-automatically using computer algorithms. ME ... Full text Link to item Cite

Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.

Journal Article J Magn Reson Imaging · October 2015 PURPOSE: To identify associations between semiautomatically extracted MRI features and breast cancer molecular subtypes. METHODS: We analyzed routine clinical pre-operative breast MRIs from 275 breast cancer patients at a single institution in this retrosp ... Full text Link to item Cite

Radiogenomics: what it is and why it is important.

Journal Article J Am Coll Radiol · August 2015 In recent years, a new direction in cancer research has emerged that focuses on the relationship between imaging phenotypes and genomics. This direction is referred to as radiogenomics or imaging genomics. The question that subsequently arises is: What is ... Full text Link to item Cite

Does Breast Imaging Experience During Residency Translate Into Improved Initial Performance in Digital Breast Tomosynthesis?

Journal Article J Am Coll Radiol · July 2015 PURPOSE: To determine the initial digital breast tomosynthesis (DBT) performance of radiology trainees with varying degrees of breast imaging experience. METHODS: To test trainee performance with DBT, we performed a reader study, after obtaining IRB approv ... Full text Link to item Cite

Modeling false positive error making patterns in radiology trainees for improved mammography education.

Journal Article J Biomed Inform · April 2015 INTRODUCTION: While mammography notably contributes to earlier detection of breast cancer, it has its limitations, including a large number of false positive exams. Improved radiology education could potentially contribute to alleviating this issue. Toward ... Full text Link to item Cite

Incorporating breast tomosynthesis into radiology residency: Does trainee experience in breast imaging translate into improved performance with this new modality?

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2015 Digital breast tomosynthesis (DBT) is a powerful new imaging modality that has the potential to transform breast cancer screening practices. The advantages over mammography include improved sensitivity and specificity as well as the detection of additional ... Full text Cite

Case-based CAD systems in breast imaging

Chapter · January 1, 2015 Knowledge-based systems (KBS) is a general term used to describe artificial intelligence techniques that rely on domain-specific knowledge to solve a new problem within that domain. The ultimate role of knowledge-based systems is to support human decision- ... Full text Cite

Computer-extracted MR imaging features are associated with survival in glioblastoma patients.

Journal Article J Neurooncol · December 2014 Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonan ... Full text Link to item Cite

Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging.

Journal Article Radiology · November 2014 PURPOSE: To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. MATERIALS AND METHODS: Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archi ... Full text Link to item Cite

Using computer-extracted image features for modeling of error-making patterns in detection of mammographic masses among radiology residents.

Journal Article Med Phys · September 2014 PURPOSE: Mammography is the most widely accepted and utilized screening modality for early breast cancer detection. Providing high quality mammography education to radiology trainees is essential, since excellent interpretation skills are needed to ensure ... Full text Link to item Cite

Radiology resident mammography training: interpretation difficulty and error-making patterns.

Journal Article Acad Radiol · July 2014 RATIONALE AND OBJECTIVES: The purpose of this study was to better understand the concept of mammography difficulty and how it affects radiology resident performance. MATERIALS AND METHODS: Seven radiology residents and three expert breast imagers reviewed ... Full text Link to item Cite

Predictors of an academic career on radiology residency applications.

Journal Article Acad Radiol · May 2014 RATIONALE AND OBJECTIVES: To evaluate radiology residency applications to determine if any variables are predictive of a future academic radiology career. MATERIALS AND METHODS: Application materials from 336 radiology residency graduates between 1993 and ... Full text Link to item Cite

A fully automatic extraction of magnetic resonance image features in glioblastoma patients.

Journal Article Med Phys · April 2014 PURPOSE: Glioblastoma is the most common malignant brain tumor. It is characterized by low median survival time and high survival variability. Survival prognosis for glioblastoma is very important for optimized treatment planning. Imaging features observed ... Full text Link to item Cite

Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features.

Journal Article Med Phys · March 2014 PURPOSE: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. METHODS: Ten radiology trainees and three expert breast im ... Full text Link to item Cite

Modeling resident error-making patterns in detection of mammographic masses using computer-extracted image features: Preliminary experiments

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2014 Providing high quality mammography education to radiology trainees is essential, as good interpretation skills potentially ensure the highest benefit of screening mammography for patients. We have previously proposed a computer-aided education system that ... Full text Cite

Imaging descriptors improve the predictive power of survival models for glioblastoma patients.

Journal Article Neuro Oncol · October 2013 BACKGROUND: Because effective prediction of survival time can be highly beneficial for the treatment of glioblastoma patients, the relationship between survival time and multiple patient characteristics has been investigated. In this paper, we investigate ... Full text Link to item Cite

Estimating confidence of individual rating predictions in collaborative filtering recommender systems

Journal Article Expert Systems with Applications · August 1, 2013 Collaborative filtering algorithms predict a rating for an item based on the user's previous ratings for other items as well as ratings of other users. This approach has gained notable popularity both in academic research and in commercial applications. On ... Full text Cite

Difficulty of mammographic cases in the context of resident training: Preliminary experimental data

Journal Article Proceedings of SPIE - The International Society for Optical Engineering · June 14, 2013 We are currently developing an intelligent data-driven educational system for mammography. Since our system attempts to predict which cases will be difficult for the trainees, it is important to better understand the concept of case difficulty. While the c ... Full text Cite

Identifying error-making patterns in assessment of mammographic BI-RADS descriptors among radiology residents using statistical pattern recognition.

Journal Article Acad Radiol · July 2012 RATIONALE AND OBJECTIVE: The objective of this study is to test the hypothesis that there are patterns in erroneous assessment of BI-RADS features among radiology trainees when interpreting mammographic masses and that these patterns can be captured in ind ... Full text Link to item Cite

The effect of class imbalance on case selection for case-based classifiers: an empirical study in the context of medical decision support.

Journal Article Neural Netw · January 2012 Case selection is a useful approach for increasing the efficiency and performance of case-based classifiers. Multiple techniques have been designed to perform case selection. This paper empirically investigates how class imbalance in the available set of t ... Full text Link to item Cite

Mutual information-based template matching scheme for detection of breast masses: from mammography to digital breast tomosynthesis.

Journal Article J Biomed Inform · October 2011 Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algori ... Full text Link to item Cite

Exploring the potential of collaborative filtering for user-adaptive mammography education

Journal Article Proceedings of the 2011 Biomedical Sciences and Engineering Conference: Image Informatics and Analytics in Biomedicine, BSEC 2011 · July 7, 2011 Specialized training in breast imaging is critical to ensure high diagnostic accuracy of the radiologists who read screening mammograms in their daily practice. Previously, we proposed a framework for an individualized computer-aided mammography training s ... Full text Cite

Modeling error in assessment of mammographic image features for improved computer-aided mammography training: Initial experience

Journal Article Progress in Biomedical Optics and Imaging - Proceedings of SPIE · May 16, 2011 In this study we investigate the hypothesis that there exist patterns in erroneous assessment of BI-RADS image features among radiology trainees when performing diagnostic interpretation of mammograms. We also investigate whether these error making pattern ... Full text Cite

Comparative analysis of instance selection algorithms for instance-based classifiers in the context of medical decision support.

Journal Article Phys Med Biol · January 21, 2011 When constructing a pattern classifier, it is important to make best use of the instances (a.k.a. cases, examples, patterns or prototypes) available for its development. In this paper we present an extensive comparative analysis of algorithms that, given a ... Full text Link to item Cite

User modeling for improved computer-aided training in radiology: Initial experience

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · December 1, 2010 Although mammography is an efficient screening modality for breast cancer, interpretation of mammographic images is a difficult task and notable variability between radiologists performance has been documented. A significant factor impacting radiologists d ... Full text Cite

Exploring the potential of context-sensitive CADe in screening mammography.

Journal Article Med Phys · November 2010 PURPOSE: Conventional computer-assisted detection (CADe) systems in screening mammography provide the same decision support to all users. The aim of this study was to investigate the potential of a context-sensitive CADe system which provides decision supp ... Full text Link to item Cite

Individualized computer-aided education in mammography based on user modeling: concept and preliminary experiments.

Journal Article Med Phys · March 2010 PURPOSE: The authors propose the framework for an individualized adaptive computer-aided educational system in mammography that is based on user modeling. The underlying hypothesis is that user models can be developed to capture the individual error making ... Full text Link to item Cite

Perception-driven IT-CADe analysis for the detection of masses in screening mammography: Initial investigation

Conference Progress in Biomedical Optics and Imaging - Proceedings of SPIE · January 1, 2010 We have previously reported an interactive information-theoretic CADe (IT-CADe) system for the detection of masses in screening mammograms. The system operates in either traditional static mode or in interactive mode whenever the user requests a second opi ... Full text Cite

The effect of class imbalance on case selection for case-based classifiers, with emphasis on computer-aided diagnosis systems

Journal Article Proceedings of the International Joint Conference on Neural Networks · November 18, 2009 In this paper the effect of class imbalance in the case base of a case-based classifier is investigated as it pertains to case base reduction and the resulting classifier performance. A k-nearest neighbor algorithm is used as a classifier and the Random Mu ... Full text Cite

Evaluating classifiers: Relation between area under the receiver operator characteristic curve and overall accuracy

Journal Article Proceedings of the International Joint Conference on Neural Networks · November 18, 2009 In this study, we investigated the relation between two popular classifier performance measures: area under the receiver operator characteristic curve and overall accuracy. We also evaluated the impact of class imbalance and number of examples in test set ... Full text Cite

Building virtual community in computational intelligence and machine learning

Journal Article IEEE Computational Intelligence Magazine · November 6, 2009 Researchers are making efforts to build a virtual community In computational intelligence (CI) and machine learning (ML). A virtual organization is a group of geographically distributed individuals or institutions that cooperate with each other concurrentl ... Full text Cite

An adaptive incremental approach to constructing ensemble classifiers: application in an information-theoretic computer-aided decision system for detection of masses in mammograms.

Journal Article Med Phys · July 2009 Ensemble classifiers have been shown efficient in multiple applications. In this article, the authors explore the effectiveness of ensemble classifiers in a case-based computer-aided diagnosis system for detection of masses in mammograms. They evaluate two ... Full text Link to item Cite

Relational representation for improved decisions with an information-theoretic CADe system: Initial experience

Journal Article Progress in Biomedical Optics and Imaging - Proceedings of SPIE · June 15, 2009 Our previously presented information-theoretic computer-aided detection (IT-CADe) system for distinguishing masses and normal parenchyma in mammograms is an example of a case-based system. IT-CAD makes decisions by evaluating the querys average similarity ... Full text Cite

A comparative study of database reduction methods for case-based computer-aided detection systems: Preliminary results

Journal Article Progress in Biomedical Optics and Imaging - Proceedings of SPIE · June 15, 2009 In case-based computer-aided decision systems (CB-CAD) a query case is compared to known examples stored in the systems case base (also called a reference library). These systems offer competitive classification performance and are easy to expand. However, ... Full text Cite

Learning in networks: Complex-valued neurons, pruning, and rule extraction

Journal Article 2008 4th International IEEE Conference Intelligent Systems, IS 2008 · December 1, 2008 This paper focuses on neural networks with complex-valued (CV) neurons as well as on selected aspects of neural networks learning, pruning and rule extraction. CV neurons can be used as versatile substitutes in real-valued perceptron networks. Learning of ... Full text Cite

Computational intelligence virtual community: Framework and implementation issues

Journal Article Proceedings of the International Joint Conference on Neural Networks · November 24, 2008 This paper discusses the framework for virtual collaborative environment for researchers, practitioners, users and learners in the areas of computational intelligence and machine learning (CIML) that is currently developed by our group. It also outlines ma ... Full text Cite

Selection of examples in case-based computer-aided decision systems.

Journal Article Phys Med Biol · November 7, 2008 Case-based computer-aided decision (CB-CAD) systems rely on a database of previously stored, known examples when classifying new, incoming queries. Such systems can be particularly useful since they do not need retraining every time a new example is deposi ... Full text Link to item Cite

Reliability assessment of ensemble classifiers: Application in mammography

Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · September 9, 2008 In classifier ensembles predictions of different classifiers regarding a query are combined into one final decision. It was previously shown that using ensemble techniques can significantly improve classification performance. In this study we build upon th ... Full text Cite

Toward perceptually driven image retrieval in mammography: A pilot observer study to assess visual similarity of masses

Journal Article Progress in Biomedical Optics and Imaging - Proceedings of SPIE · June 18, 2008 Development of a fully automated system retrieving visually similar images is a task that could be helpful as the basis of a computer-assisted diagnostic (CADx) tool in mammography. Our study aims at a better understanding of the concept of visual similari ... Full text Cite

Database decomposition of a knowledge-based CAD system in mammography; An ensemble approach to improve detection

Journal Article Progress in Biomedical Optics and Imaging - Proceedings of SPIE · June 2, 2008 Although ensemble techniques have been investigated in supervised machine learning, their potential with knowledge-based systems is unexplored. The purpose of this study is to investigate the ensemble approach with a knowledge-based (KB) CAD system for the ... Full text Cite

Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography.

Journal Article Phys Med Biol · February 21, 2008 This paper presents an optimization framework for improving case-based computer-aided decision (CB-CAD) systems. The underlying hypothesis of the study is that each example in the knowledge database of a medical decision support system has different import ... Full text Link to item Cite

Training neural network classifiers for medical decision making: the effects of imbalanced datasets on classification performance.

Journal Article Neural Netw · 2008 This study investigates the effect of class imbalance in training data when developing neural network classifiers for computer-aided medical diagnosis. The investigation is performed in the presence of other characteristics that are typical among medical d ... Full text Link to item Cite

Solving decentralized multi-agent control problems with genetic algorithms

Journal Article 2007 IEEE Congress on Evolutionary Computation, CEC 2007 · December 1, 2007 In decentralized control of multi-agent systems each agent is making a decision regarding its action autonomously, based on its own observations. In the light of the formal models of decentralized environments presented in the last decade, finding an optim ... Full text Cite

Case-base reduction for a computer assisted breast cancer detection system using genetic algorithms

Journal Article 2007 IEEE Congress on Evolutionary Computation, CEC 2007 · December 1, 2007 A knowledge-based computer assisted decision (KB-CAD) system is a case-based reasoning system previously proposed for breast cancer detection. Although it was demonstrated to be very effective for the diagnostic problem, it was also shown to be computation ... Full text Cite

Impact of low class prevalence on the performance evaluation of neural network based classifiers: Experimental study in the context of computer-assisted medical diagnosis

Journal Article IEEE International Conference on Neural Networks - Conference Proceedings · December 1, 2007 This paper presents an experimental study on the impact of low class prevalence on the neural network based classifier performance as measured using Receiver Operator Characteristic (ROC) analysis. Two methods of dealing with the problem are investigated: ... Full text Cite

Stacked generalization in computer-assisted decision systems: Empirical comparison of data handling schemes

Journal Article IEEE International Conference on Neural Networks - Conference Proceedings · December 1, 2007 Computer-assisted decision (CAD) systems are becoming increasingly popular for the diagnostic interpretation of radiologic images. These CAD systems often involve the stacked generalization of several different decision models. Combining decision models is ... Full text Cite

Solving multi-agent control problems using particle swarm optimization

Journal Article Proceedings of the 2007 IEEE Swarm Intelligence Symposium, SIS 2007 · September 25, 2007 This paper outlines an approximate algorithm for finding an optimal decentralized control in multi-agent systems. Decentralized Partially Observable Markov Decision Processes and their extension to infinite state, observation and action spaces are utilized ... Full text Cite

Emergence of communication in multi-agent systems using reinforcement learning

Journal Article 2006 IEEE International Conference on Computational Cybernetics, ICCC · December 1, 2006 In this paper, the new approach to the emergence of communication between autonomous agents is introduced. The learning scheme is presented, which allows for emergence of efficient communication between agents in cooperative systems. Classical reinforcemen ... Full text Cite

Limitations of sensitivity analysis for neural networks in cases with dependent inputs

Journal Article 2006 IEEE International Conference on Computational Cybernetics, ICCC · December 1, 2006 In this paper the limitations of the sensitivity analysis method for feedforward neural networks in the cases of dependent input variables are discussed. First, it is explained that in such cases there can be many functions implemented by neural networks t ... Full text Cite

Neural network sensitivity analysis applied for the reduction of the sensor matrix

Journal Article Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) · January 1, 2005 The neural network sensitivity analysis, involving neural network training and the calculation of its outputs derivative on inputs, was applied to select the least significant sensor in the multicomponont gas mixtures annlysis system. The sensitivity analy ... Full text Cite