Deep Learning

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  Subject Areas on Research

  • A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss From Optic Disc Photographs. 
  • A convolutional neural network to filter artifacts in spectroscopic MRI. 
  • AI for medical imaging goes deep. 
  • Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. 
  • Artificial intelligence and deep learning in ophthalmology. 
  • 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. 
  • 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 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 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. 
  • Detecting Retinal Nerve Fibre Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm. 
  • Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. 
  • Eyeing cardiovascular risk factors. 
  • Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning. 
  • From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs. 
  • Fully Automated Segmentation of Globes for Volume Quantification in CT Images of Orbits using Deep Learning. 
  • 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. 
  • 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. 
  • Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images. 
  • On Artificial Intelligence and Deep Learning Within Medical Education. 
  • On Deep Learning for Medical Image Analysis. 
  • Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach. 
  • 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. 
  • 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. 
  • Using Deep Learning to Automate Goldmann Applanation Tonometry Readings. 
  • Validation of a fully automated liver segmentation algorithm using multi-scale deep reinforcement learning and comparison versus manual segmentation. 
  • Will AI Improve Tumor Delineation Accuracy for Radiation Therapy?