Deep Learning

• 

  Subject Areas on Research

  • 3Din vivodose verification in prostate proton therapy with deep learning-based proton-acoustic imaging. 
  • A Conference-Friendly, Hands-on Introduction to Deep Learning for Radiology Trainees. 
  • A Data Set and Deep Learning Algorithm for the Detection of Masses and Architectural Distortions in Digital Breast Tomosynthesis Images. 
  • A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs. 
  • A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression. 
  • A convolutional neural network to filter artifacts in spectroscopic MRI. 
  • A deep learning approach to student registered nurse anesthetist (SRNA) education. 
  • A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. 
  • A geometry-guided deep learning technique for CBCT reconstruction. 
  • A geometry-guided multi-beamlet deep learning technique for CT reconstruction. 
  • A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images. 
  • A unified framework for personalized regions selection and functional relation modeling for early MCI identification. 
  • AI for medical imaging goes deep. 
  • An index based on deep learning-measured spleen volume on CT for the assessment of high-risk varix in B-viral compensated cirrhosis. 
  • An objective structural and functional reference standard in glaucoma. 
  • Applications of deep learning in detection of glaucoma: A systematic review. 
  • Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. 
  • Artificial intelligence and deep learning in ophthalmology. 
  • Artificial intelligence for automatic cerebral ventricle segmentation and volume calculation: a clinical tool for the evaluation of pediatric hydrocephalus. 
  • Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study. 
  • Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans. 
  • Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm. 
  • Augmentation of CBCT Reconstructed From Under-Sampled Projections Using Deep Learning. 
  • AutoAudio: Deep Learning for Automatic Audiogram Interpretation. 
  • Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning. 
  • Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning. 
  • Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome. 
  • COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model. 
  • Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. 
  • Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center. 
  • Comprehensive functional genomic resource and integrative model for the human brain. 
  • Computation of transcranial magnetic stimulation electric fields using self-supervised deep learning. 
  • Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images. 
  • Deep Learning Computer-aided Polyp Detection Reduces Adenoma Miss Rate: A United States Multi-center Randomized Tandem Colonoscopy Study (CADeT-CS Trial). 
  • Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs. 
  • Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study. 
  • Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting. 
  • Deep Learning-Based Assessment of Functional Liver Capacity Using Gadoxetic Acid-Enhanced Hepatobiliary Phase MRI. 
  • Deep Learning-Based Automatic Assessment of Radiation Dermatitis in Patients With Nasopharyngeal Carcinoma. 
  • Deep Learning-Based Computed Tomography Perfusion Mapping (DL-CTPM) for Pulmonary CT-to-Perfusion Translation. 
  • Deep Learning-Based Risk Model for Best Management of Closed Groin Incisions After Vascular Surgery. 
  • Deep Learning-Based Segmentation of Nodules in Thyroid Ultrasound: Improving Performance by Utilizing Markers Present in the Images. 
  • Deep convolutional neural networks to predict cardiovascular risk from computed tomography. 
  • Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. 
  • Deep learning approaches for plethysmography signal quality assessment in the presence of atrial fibrillation. 
  • Deep learning based spectral extrapolation for dual-source, dual-energy x-ray computed tomography. 
  • Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. 
  • Deep learning for identifying radiogenomic associations in breast cancer. 
  • Deep learning for the dynamic prediction of multivariate longitudinal and survival data. 
  • Deep learning in glaucoma: progress, but still lots to do. 
  • Deep learning in ophthalmology: The technical and clinical considerations. 
  • Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI. 
  • Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes. 
  • Deep learning on time series laboratory test results from electronic health records for early detection of pancreatic cancer. 
  • Deep learning-based AI model for signet-ring cell carcinoma diagnosis and chemotherapy response prediction in gastric cancer. 
  • Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. 
  • Deep learning-based classification and segmentation of retinal cavitations on optical coherence tomography images of macular telangiectasia type 2. 
  • Deep learning-based quantification of NAFLD/NASH progression in human liver biopsies. 
  • Deep recurrent model for individualized prediction of Alzheimer's disease progression. 
  • Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies. 
  • Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI. 
  • DeepProjection: specific and robust projection of curved 2D tissue sheets from 3D microscopy using deep learning. 
  • Deploying artificial intelligence to find the needle in the haystack: deep learning for video capsule endoscopy. 
  • Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm. 
  • Detection of Lung Nodules in Micro-CT Imaging Using Deep Learning. 
  • Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning. 
  • Development and evaluation of deep learning-based segmentation of histologic structures in the kidney cortex with multiple histologic stains. 
  • Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. 
  • Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy. 
  • Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. 
  • Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. 
  • Effectiveness of a Deep-learning Polyp Detection System in Prospectively Collected Colonoscopy Videos With Variable Bowel Preparation Quality. 
  • Enhancing digital tomosynthesis (DTS) for lung radiotherapy guidance using patient-specific deep learning model. 
  • Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study. 
  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. 
  • Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning. 
  • Forty-two Million Ways to Describe Pain: Topic Modeling of 200,000 PubMed Pain-Related Abstracts Using Natural Language Processing and Deep Learning-Based Text Generation. 
  • From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs. 
  • Geometric deep learning enables 3D kinematic profiling across species and environments. 
  • Head CT deep learning model is highly accurate for early infarct estimation. 
  • Human Versus Machine: Comparing a Deep Learning Algorithm to Human Gradings for Detecting Glaucoma on Fundus Photographs. 
  • Identifying Smoking Environments From Images of Daily Life With Deep Learning. 
  • Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks. 
  • Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy. 
  • Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media. 
  • Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization. 
  • Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images. 
  • Lower Adenoma Miss Rate of Computer-Aided Detection-Assisted Colonoscopy vs Routine White-Light Colonoscopy in a Prospective Tandem Study. 
  • MRI-Based Deep Learning Segmentation and Radiomics of Sarcoma in Mice. 
  • MRI-based treatment planning for liver stereotactic body radiotherapy: validation of a deep learning-based synthetic CT generation method. 
  • MRI-based treatment planning for proton radiotherapy: dosimetric validation of a deep learning-based liver synthetic CT generation method. 
  • Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial. 
  • Management of Thyroid Nodules Seen on US Images: Deep Learning May Match Performance of Radiologists. 
  • Monitoring significant ST changes through deep learning. 
  • Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning. 
  • Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. 
  • Multiresolution residual deep neural network for improving pelvic CBCT image quality. 
  • Neural network approximation: Three hidden layers are enough. 
  • Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. 
  • Novel deep neural network based pattern field classification architectures. 
  • On Artificial Intelligence and Deep Learning Within Medical Education. 
  • On Deep Learning for Medical Image Analysis. 
  • Open-Source Automatic Segmentation of Ocular Structures and Biomarkers of Microbial Keratitis on Slit-Lamp Photography Images Using Deep Learning. 
  • Open-source deep learning-based automatic segmentation of mouse Schlemm's canal in optical coherence tomography images. 
  • Patient-specific deep learning model to enhance 4D-CBCT image for radiomics analysis. 
  • Population-Scale CT-based Body Composition Analysis of a Large Outpatient Population Using Deep Learning to Derive Age-, Sex-, and Race-specific Reference Curves. 
  • Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach. 
  • Predicting Age From Optical Coherence Tomography Scans With Deep Learning. 
  • Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs. 
  • Predicting Molecular Subtype and Survival of Rhabdomyosarcoma Patients Using Deep Learning of H&E Images: A Report from the Children's Oncology Group. 
  • Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. 
  • Prediction of Polyp Pathology Using Convolutional Neural Networks Achieves "Resect and Discard" Thresholds. 
  • Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. 
  • QuickNAT: A fully convolutional network for quick and accurate segmentation of neuroanatomy. 
  • Rapid Estimation of Entire Brain Strain Using Deep Learning Models. 
  • Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs. 
  • Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning. 
  • Reduced-Dose Deep Learning Reconstruction for Abdominal CT of Liver Metastases. 
  • RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure. 
  • Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images. 
  • Sounding out the hidden data: A concise review of deep learning in photoacoustic imaging. 
  • Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. 
  • Spinal cord gray matter segmentation using deep dilated convolutions. 
  • Subject Matter Knowledge in the Age of Big Data and Machine Learning. 
  • TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile. 
  • The role of machine and deep learning in modern medical physics. 
  • Toward an interpretable Alzheimer's disease diagnostic model with regional abnormality representation via deep learning. 
  • Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction. 
  • Using Deep Learning to Automate Goldmann Applanation Tonometry Readings. 
  • VALIDATION OF A DEEP LEARNING-BASED ALGORITHM FOR SEGMENTATION OF THE ELLIPSOID ZONE ON OPTICAL COHERENCE TOMOGRAPHY IMAGES OF AN USH2A-RELATED RETINAL DEGENERATION CLINICAL TRIAL. 
  • Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?