Deep Learning

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  Subject Areas on Research

  • 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 system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre. 
  • AI for medical imaging goes deep. 
  • 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 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. 
  • 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. 
  • 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. 
  • 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 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-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists. 
  • Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies. 
  • Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm. 
  • 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. 
  • Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. 
  • 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 
  • 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. 
  • Lightweight Learning-Based Automatic Segmentation of Subretinal Blebs on Microscope-Integrated Optical Coherence Tomography Images. 
  • 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. 
  • MVP predicts the pathogenicity of missense variants by deep learning. 
  • 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. 
  • Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. 
  • Noise and spatial resolution properties of a commercially available deep learning-based CT reconstruction algorithm. 
  • On Artificial Intelligence and Deep Learning Within Medical Education. 
  • On Deep Learning for Medical Image 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. 
  • Prediction of Occult Invasive Disease in Ductal Carcinoma in Situ Using Deep Learning Features. 
  • 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. 
  • Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs. 
  • Reconstructing Undersampled Photoacoustic Microscopy Images Using Deep Learning. 
  • Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. 
  • Subject Matter Knowledge in the Age of Big Data and Machine Learning. 
  • The role of machine and deep learning in modern medical physics. 
  • Using Deep Learning to Automate Goldmann Applanation Tonometry Readings. 
  • Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?