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?