Kyle Jon Lafata

Journal Article J Am Coll Radiol · October 2025 OBJECTIVES: Differences in CT-based body composition (BC) have been observed by race. We sought to investigate whether indices reporting census block group-level disadvantage, Area Deprivation Index (ADI) and Social Vulnerability Index (SVI), age, gender, ... Full text Link to item Cite

Development and application of a novel tumor habitat analysis technique based on dynamical modeling.

Journal Article Med Phys · September 2025 BACKGROUND: Oropharyngeal cancer (OPC) exhibits varying responses to chemoradiation therapy, making treatment outcome prediction challenging. Traditional imaging-based methods often fail to capture the spatial heterogeneity within tumors, which influences ... Full text Link to item Cite

Clinical relevance of computationally derived tubular features and their spatial relationships with the interstitial microenvironment in minimal change disease/focal segmental glomerulosclerosis.

Journal Article Kidney Int · August 2025 BACKGROUND: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. Here, we investigated if computationally quantified tubular features can enhance prognostication and reveal spatial ... Full text Link to item Cite

The Duke Lung Cancer Screening (DLCS) Dataset: A Reference Dataset of Annotated Low-Dose Screening Thoracic CT.

Journal Article Radiol Artif Intell · July 2025 The Duke Lung Cancer Screening (DLCS) Dataset contains is a large collection of lung cancer screening low-dose CT scans for lung nodule classification with annotations performed in a semi-automated manner, requiring substantially reduced radiologist effort ... Full text Link to item Cite

XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans.

Journal Article Med Image Anal · July 2025 Virtual Imaging Trials (VIT) offer a cost-effective and scalable approach for evaluating medical imaging technologies. Computational phantoms, which mimic real patient anatomy and physiology, play a central role in VITs. However, the current libraries of c ... Full text Link to item Cite

Virtual Lung Screening Trial (VLST): An In Silico Study Inspired by the National Lung Screening Trial for Lung Cancer Detection.

Journal Article ArXiv · April 4, 2025 Clinical imaging trials play a crucial role in advancing medical innovation but are often costly, inefficient, and ethically constrained. Virtual Imaging Trials (VITs) present a solution by simulating clinical trial components in a controlled, risk-free en ... Link to item Cite

Radiogenomic explainable AI with neural ordinary differential equation for identifying post-SRS brain metastasis radionecrosis.

Conference Med Phys · April 2025 BACKGROUND: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinic ... Full text Link to item Cite

Concordance-based Predictive Uncertainty (CPU)-Index: Proof-of-concept with application towards improved specificity of lung cancers on low dose screening CT.

Journal Article Artif Intell Med · February 2025 In this paper, we introduce a novel concordance-based predictive uncertainty (CPU)-Index, which integrates insights from subgroup analysis and personalized AI time-to-event models. Through its application in refining lung cancer screening (LCS) predictions ... Full text Link to item Cite

The Role of Harmonization: A Systematic Analysis of Various Task-based Scenarios.

Journal Article Proc SPIE Int Soc Opt Eng · February 2025 In medical imaging, harmonization plays a crucial role in reducing variability arising from diverse imaging devices and protocols. Patient images obtained under different computed tomography (CT) scan conditions may show varying performance when analyzed u ... Full text Link to item Cite

Long-term, automated stool monitoring using a novel smart toilet: A feasibility study.

Journal Article Neurogastroenterol Motil · January 2025 BACKGROUND: Patients' report of bowel movement consistency is unreliable. We demonstrate the feasibility of long-term automated stool image data collection using a novel Smart Toilet and evaluate a deterministic computer-vision analytic approach to assess ... Full text Link to item Cite

Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation.

Journal Article Front Oncol · 2025 PURPOSE: This work investigates the use of a spherical projection-based U-Net (SPU-Net) segmentation model to improve meningioma segmentation performance and allow for uncertainty quantification. METHODS: A total of 76 supratentorial meningioma patients tr ... Full text Link to item Cite

Clinical Relevance of Computational Pathology Analysis of Interplay Between Kidney Microvasculature and Interstitial Microenvironment.

Journal Article Clin J Am Soc Nephrol · December 23, 2024 BACKGROUND: Interstitial fibrosis and tubular atrophy (IFTA), and density and shape of peritubular capillaries (PTCs), are independently prognostic of disease progression. This study aimed to identify novel digital biomarkers of disease progression and ass ... Full text Link to item Cite

Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.

Journal Article Int J Radiat Oncol Biol Phys · October 1, 2024 PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS). METHODS AND MATERIALS: A total of 114 brain metastases (BMs) from 82 patients were evalu ... Full text Link to item Cite

Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics

Journal Article Biocybernetics and Biomedical Engineering · July 1, 2024 For individuals with Type-1 diabetes mellitus, accurate prediction of future blood glucose values is crucial to aid its regulation with insulin administration, tailored to the individual's specific needs. The authors propose a novel approach for the integr ... Full text Cite

Radiomics on spatial-temporal manifolds via Fokker-Planck dynamics.

Conference Med Phys · May 2024 BACKGROUND: Delta radiomics is a high-throughput computational technique used to describe quantitative changes in serial, time-series imaging by considering the relative change in radiomic features of images extracted at two distinct time points. Recent wo ... Full text Link to item Cite

Lung Cancer Screening in Clinical Practice: A 5-Year Review of Frequency and Predictors of Lung Cancer in the Screened Population.

Journal Article J Am Coll Radiol · May 2024 PURPOSE: The aims of this study were to evaluate (1) frequency, type, and lung cancer stage in a clinical lung cancer screening (LCS) population and (2) the association between patient characteristics and Lung CT Screening Reporting & Data System (Lung-RAD ... Full text Link to item Cite

Quantifying U-Net uncertainty in multi-parametric MRI-based glioma segmentation by spherical image projection.

Journal Article Med Phys · March 2024 BACKGROUND: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In pot ... Full text Link to item Cite

Prognostic value of different discretization parameters in 18fluorodeoxyglucose positron emission tomography radiomics of oropharyngeal squamous cell carcinoma.

Journal Article J Med Imaging (Bellingham) · March 2024 PURPOSE: We aim to interrogate the role of positron emission tomography (PET) image discretization parameters on the prognostic value of radiomic features in patients with oropharyngeal cancer. APPROACH: A prospective clinical trial (NCT01908504) enrolled ... Full text Link to item Cite

Virtual NLST: Towards Replicating National Lung Screening Trial

Conference Progress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2024 Virtual Imaging Trials, known as VITs, provide a computational substitute for clinical trials. These traditional trials tend to be sluggish, costly, and frequently deficient in definitive evidence, all the while subjecting participants to ionizing radiatio ... Full text Cite

Artificial intelligence across oncology specialties: current applications and emerging tools.

Journal Article BMJ Oncol · 2024 Oncology is becoming increasingly personalised through advancements in precision in diagnostics and therapeutics, with more and more data available on both ends to create individualised plans. The depth and breadth of data are outpacing our natural ability ... Full text Link to item Cite

Inference Serving System for Stable Diffusion as a Service

Conference Proceeding 2024 IEEE Cloud Summit Cloud Summit 2024 · January 1, 2024 We present a model-less, privacy-preserving, low-latency inference framework to satisfy user-defined System-Level Objectives (SLO) for Stable Diffusion as a Service (SDaaS). Developers of Stable Diffusion (SD) models register their trained models on our pr ... Full text Cite

Computational staining of CD3/CD20 positive lymphocytes in human tissues with experimental confirmation in a genetically engineered mouse model.

Journal Article Front Immunol · 2024 INTRODUCTION: Immune dysregulation plays a major role in cancer progression. The quantification of lymphocytic spatial inflammation may enable spatial system biology, improve understanding of therapeutic resistance, and contribute to prognostic imaging bio ... Full text Link to item Cite

Neural Architecture Search for Blood Glucose Prediction in Type-1 Diabetics

Conference 2024 IEEE 20th International Conference on Body Sensor Networks Bsn 2024 Proceedings · January 1, 2024 For subjects affected with type-1 diabetes mellitus, accurately predicting future blood glucose values helps regulate insulin delivery. This paper introduces a dual Q-network-based neural architecture search approach to develop and train per-sonalized BG p ... Full text Cite

Preserving Accuracy While Stealing Watermarked Deep Neural Networks

Conference Proceedings 2024 International Conference on Machine Learning and Applications Icmla 2024 · January 1, 2024 The deployment of Deep Neural Networks (DNNs) as cloud services has accelerated significantly over the years. Training an application-specific DNN for cloud deployment requires substantial computational resources and costs associated with hyper-parameter t ... Full text Cite

Digital staining in optical microscopy using deep learning - a review

Journal Article Photonix · December 1, 2023 Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining pro ... Full text Cite

A neural ordinary differential equation model for visualizing deep neural network behaviors in multi-parametric MRI-based glioma segmentation.

Journal Article Med Phys · August 2023 PURPOSE: To develop a neural ordinary differential equation (ODE) model for visualizing deep neural network behavior during multi-parametric MRI-based glioma segmentation as a method to enhance deep learning explainability. METHODS: By hypothesizing that d ... Full text Link to item Cite

A Faster Prostate MRI: Comparing a Novel Denoised, Single-Average T2 Sequence to the Conventional Multiaverage T2 Sequence Regarding Lesion Detection and PI-RADS Score Assessment.

Journal Article J Magn Reson Imaging · August 2023 BACKGROUND: The T2 w sequence is a standard component of a prostate MRI examination; however, it is time-consuming, requiring multiple signal averages to achieve acceptable image quality. PURPOSE/HYPOTHESIS: To determine whether a denoised, single-average ... Full text Link to item Cite

Exploratory analysis of mesenteric-portal axis CT radiomic features for survival prediction of patients with pancreatic ductal adenocarcinoma.

Journal Article Eur Radiol · August 2023 OBJECTIVE: To develop and evaluate task-based radiomic features extracted from the mesenteric-portal axis for prediction of survival and response to neoadjuvant therapy in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: Consecutive patients ... Full text Link to item Cite

Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.

Journal Article Med Phys · June 2023 BACKGROUND: Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with clinical data can be non-trivial. Optimal integration requires robust data fusion, that is, the process o ... Full text Link to item Cite

Systematic Analysis of Common Factors Impacting Deep Learning Model Generalizability in Liver Segmentation.

Journal Article Radiol Artif Intell · May 2023 PURPOSE: To investigate the effect of training data type on generalizability of deep learning liver segmentation models. MATERIALS AND METHODS: This Health Insurance Portability and Accountability Act-compliant retrospective study included 860 MRI and CT a ... Full text Link to item Cite

Clinical Relevance of Computationally Derived Attributes of Peritubular Capillaries from Kidney Biopsies.

Journal Article Kidney360 · May 1, 2023 KEY POINTS: Computational image analysis allows for the extraction of new information from whole-slide images with potential clinical relevance. Peritubular capillary (PTC) density is decreased in areas of interstitial fibrosis and tubular atrophy when mea ... Full text Link to item Cite

Comparing Survival Outcomes of Patients With LI-RADS-M Hepatocellular Carcinomas and Intrahepatic Cholangiocarcinomas.

Journal Article J Magn Reson Imaging · January 2023 BACKGROUND: There is a sparsity of data evaluating outcomes of patients with Liver Imaging Reporting and Data System (LI-RADS) (LR)-M lesions. PURPOSE: To compare overall survival (OS) and progression free survival (PFS) between hepatocellular carcinoma (H ... Full text Link to item Cite

CT-derived body composition measurements as predictors for neoadjuvant treatment tolerance and survival in gastroesophageal adenocarcinoma.

Journal Article Abdom Radiol (NY) · January 2023 PURPOSE: Treatment for gastroesophageal adenocarcinomas can result in significant morbidity and mortality. The purpose of this study is to supplement methods for choosing treatment strategy by assessing the relationship between CT-derived body composition, ... Full text Link to item Cite

Decoding the Encoder

Conference Conference Proceedings IEEE SOUTHEASTCON · January 1, 2023 Autoencoders are used in a variety of safety-critical applications. Uncertainty quantification is a key component to bolster the trustworthiness of such models. With the growing complexity of the autoencoder design and the dataset they are trained on, ther ... Full text Cite

Automatic quality control in computed tomography volumes segmentation using a small set of XCAT as reference images

Conference Progress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2023 Deep learning methods have performed superiorly to segment organs of interest from Computed Tomography images than traditional methods. However, the trained models do not generalize well at the inference phase, and manual validation and correction are not ... Full text Cite

Privacy-preserving Job Scheduler for GPU Sharing

Conference Proceedings 23rd IEEE ACM International Symposium on Cluster Cloud and Internet Computing Workshops Ccgridw 2023 · January 1, 2023 Machine learning (ML) training jobs are resource intensive. High infrastructure costs of computing clusters encourage multi-tenancy in GPU resources. This invites a scheduling problem in assigning multiple ML training jobs on a single GPU while minimizing ... Full text Cite

Job Recommendation Service for GPU Sharing in Kubernetes

Conference Proceedings 2023 IEEE Cloud Summit Cloud Summit 2023 · January 1, 2023 Cloud infrastructures encourage the multi-tenancy of hardware resources. User-defined Machine Learning (ML) training jobs are offloaded to the cloud for efficient training. State-of-the-art resource schedulers do not preserve user privacy by accessing sens ... Full text Cite

Development of a multi-feature-combined model: proof-of-concept with application to local failure prediction of post-SBRT or surgery early-stage NSCLC patients.

Journal Article Front Oncol · 2023 OBJECTIVE: To develop a Multi-Feature-Combined (MFC) model for proof-of-concept in predicting local failure (LR) in NSCLC patients after surgery or SBRT using pre-treatment CT images. This MFC model combines handcrafted radiomic features, deep radiomic fea ... Full text Link to item Cite

Blood Glucose Prediction for Type-1 Diabetics using Deep Reinforcement Learning

Conference Proceedings 2023 IEEE International Conference on Digital Health Icdh 2023 · January 1, 2023 An accurate prediction of blood glucose levels for individuals affected with type-1 diabetes mellitus helps to regulate blood glucose through specific insulin delivery. In our work, we propose the design of a densely-connected encoder-decoder network in co ... Full text Cite

Quantification of lung function on CT images based on pulmonary radiomic filtering.

Journal Article Med Phys · November 2022 PURPOSE: To develop a radiomics filtering technique for characterizing spatial-encoded regional pulmonary ventilation information on lung computed tomography (CT). METHODS: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was i ... Full text Link to item Cite

Prognostic Model for Intracranial Progression after Stereotactic Radiosurgery: A Multicenter Validation Study.

Journal Article Cancers (Basel) · October 22, 2022 Stereotactic radiosurgery (SRS) is a standard of care for many patients with brain metastases. To optimize post-SRS surveillance, this study aimed to validate a previously published nomogram predicting post-SRS intracranial progression (IP). We identified ... Full text Link to item Cite

Radiomics: a primer on high-throughput image phenotyping.

Journal Article Abdom Radiol (NY) · September 2022 Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve ... Full text Link to item Cite

Modifying LI-RADS on Gadoxetate Disodium-Enhanced MRI: A Secondary Analysis of a Prospective Observational Study.

Journal Article J Magn Reson Imaging · August 2022 BACKGROUND: The Liver Imaging Reporting and Data System (LI-RADS) is widely used for diagnosing hepatocellular carcinoma (HCC), however, with unsatisfactory sensitivity, complex ancillary features, and inadequate integration with gadoxetate disodium (EOB)- ... Full text Link to item Cite

Can radiomic analysis of a single-phase dual-energy CT improve the diagnostic accuracy of differentiating enhancing from non-enhancing small renal lesions?

Journal Article Acta Radiol · June 2022 BACKGROUND: The value of dual-energy computed tomography (DECT)-based radiomics in renal lesions is unknown. PURPOSE: To develop DECT-based radiomic models and assess their incremental values in comparison to conventional measurements for differentiating e ... Full text Link to item Cite

A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.

Conference Med Phys · May 2022 PURPOSE: To develop a deep learning model design that integrates radiomics analysis for enhanced performance of COVID-19 and non-COVID-19 pneumonia detection using chest x-ray images. METHODS: As a novel radiomics approach, a 2D sliding kernel was implemen ... Full text Link to item Cite

A Deep Learning Model with Radiomics Analysis Integration for Glioblastoma Post-Resection Survival Prediction

Preprint · March 11, 2022 Link to item Cite

Photon Counting CT and Radiomic Analysis Enables Differentiation of Tumors Based on Lymphocyte Burden.

Journal Article Tomography · March 10, 2022 The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on lymphocyte burden. High mutational load transplant soft tissue sarcomas were initiated in ... Full text Open Access Link to item Cite

Data-Driven Modification of the LI-RADS Major Feature System on Gadoxetate Disodium-Enhanced MRI: Toward Better Sensitivity and Simplicity.

Journal Article Journal of magnetic resonance imaging : JMRI · February 2022 BackgroundThe Liver Imaging Reporting and Data System (LI-RADS) is widely accepted as a reliable diagnostic scheme for hepatocellular carcinoma (HCC) in at-risk patients. However, its application is hampered by substantial complexity and suboptima ... Full text Cite

The Role of Machine Learning in Cardiovascular Pathology.

Journal Article Can J Cardiol · February 2022 Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but the ... Full text Link to item Cite

Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application.

Journal Article Front Oncol · 2022 PURPOSE: To develop a method of biologically guided deep learning for post-radiation 18FDG-PET image outcome prediction based on pre-radiation images and radiotherapy dose information. METHODS: Based on the classic reaction-diffusion mechanism, a novel bio ... Full text Link to item Cite

Spectral micro-CT and nanoradiomic analysis for classification of tumors based on lymphocytic burden in cancer therapy studies

Conference Progress in Biomedical Optics and Imaging Proceedings of SPIE · January 1, 2022 The purpose of this study was to investigate if radiomic analysis based on spectral micro-CT with nanoparticle contrast-enhancement can differentiate tumors based on tumor-infiltrating lymphocyte (TIL) burden. High mutational load transplant soft tissue sa ... Full text Cite

CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study.

Journal Article Radiology · December 2021 Background Current imaging methods for prediction of complete margin resection (R0) in patients with pancreatic ductal adenocarcinoma (PDAC) are not reliable. Purpose To investigate whether tumor-related and perivascular CT radiomic features improve preope ... Full text Open Access Link to item Cite

Deep learning segmentation of glomeruli on kidney donor frozen sections.

Journal Article J Med Imaging (Bellingham) · November 2021 Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and s ... Full text Link to item Cite

Week 4 Liver Fat Reduction on MRI as an Early Predictor of Treatment Response in Participants with Nonalcoholic Steatohepatitis.

Journal Article Radiology · August 2021 Background Pharmacologic treatment of nonalcoholic steatohepatitis (NASH) is long term in nature; thus, early noninvasive treatment response assessment is important for therapeutic decision making. Purpose To investigate potential early predictors of the 1 ... Full text Link to item Cite

Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers.

Journal Article Med Phys · July 2021 PURPOSE: This study investigated the prognostic potential of intra-treatment PET radiomics data in patients undergoing definitive (chemo) radiation therapy for oropharyngeal cancer (OPC) on a prospective clinical trial. We hypothesized that the radiomic ex ... Full text Link to item Cite

A generative adversarial network (GAN)-based technique for synthesizing realistic respiratory motion in the extended cardiac-torso (XCAT) phantoms.

Journal Article Phys Med Biol · May 31, 2021 Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bic ... Full text Link to item Cite

Radiogenomic Analysis of Locally Advanced Lung Cancer Based on CT Imaging and Intratreatment Changes in Cell-Free DNA.

Journal Article Radiology. Imaging cancer · April 2021 The radiologic appearance of locally advanced lung cancer may be linked to molecular changes of the disease during treatment, but characteristics of this phenomenon are poorly understood. Radiomics, liquid biopsy of cell-free DNA (cfDNA), and next-generati ... Full text Cite

Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases

Conference MODERN PATHOLOGY · 2021 Cite

Cortical Tubulointerstitial Mononuclear Inflammation in Renal Biopsies is a Quantitative Biomarker of Clinical Outcomes in NEPTUNE Glomerular Diseases

Conference LABORATORY INVESTIGATION · 2021 Cite

Computer-Assisted Diagnosis of Hepatic Portal Hypertension: A Novel, Attention-Guided Deep Learning Framework Based On CT Imaging and Laboratory Data Integration

Conference MEDICAL PHYSICS · 2021 Cite

Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma

Conference MEDICAL PHYSICS · 2021 Cite

Measuring Distribution of An I-125 Labeled Elastin-Like Polypeptide (ELP) Nanoparticle Within Mice Tumors for Consideration as a Novel Technique of Delivering Brachytherapy

Conference MEDICAL PHYSICS · 2021 Cite

Biologically Guided Deep Learning for Post-Radiation PET Image Outcome Prediction: A Feasibility Study of Oropharyngeal Cancer Application

Conference MEDICAL PHYSICS · 2021 Cite

Digital pathology and computational image analysis in nephropathology.

Journal Article Nat Rev Nephrol · November 2020 The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In par ... Full text Link to item Cite

Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data.

Journal Article Quant Imaging Med Surg · October 2020 BACKGROUND: To develop a high-efficiency pulmonary nodule computer-aided detection (CAD) method for localization and diameter estimation. METHODS: The developed CAD method centralizes a novel convolutional neural network (CNN) algorithm, You Only Look Once ... Full text Link to item Cite

Development of realistic multi-contrast textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN).

Journal Article Phys Med Biol · March 19, 2020 Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For tra ... Full text Link to item Cite

Digital phantoms for characterizing inconsistencies among radiomics extraction toolboxes.

Journal Article Biomed Phys Eng Express · March 2, 2020 PURPOSE: to develop digital phantoms for characterizing inconsistencies among radiomics extraction methods based on three radiomics toolboxes: CERR (Computational Environment for Radiological Research), IBEX (imaging biomarker explorer), and an in-house ra ... Full text Link to item Cite

A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections

Conference LABORATORY INVESTIGATION · March 1, 2020 Link to item Cite

A Deep Learning Approach to Analysis of Kidney Transplant Frozen Sections

Conference MODERN PATHOLOGY · March 1, 2020 Link to item Cite

Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application

Conference Frontiers in oncology · January 2020 Purpose To develop a deep learning-based AI agent, DDD-PIOP (Dose-Distribution-Driven PET Image Outcome Prediction), for predicting 18FDG-PET image outcomes of oropharyngeal cancer (OPC) in response to intensity-modulated radiation therapy (IMRT). Methods ... Cite

Dose-Distribution-Driven PET Image-Based Outcome Prediction (DDD-PIOP): A Deep Learning Study for Oropharyngeal Cancer IMRT Application.

Journal Article Front Oncol · 2020 PURPOSE: To develop a deep learning-based AI agent, DDD-PIOP (Dose-Distribution-Driven PET Image Outcome Prediction), for predicting 18FDG-PET image outcomes of oropharyngeal cancer (OPC) in response to intensity-modulated radiation therapy (IMRT). METHODS ... Full text Open Access Link to item Cite