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AISINAI
Mount Sinai AI Consortium
An attention based deep learning model of clinical events in the intensive care unit - PLOS ONE
Eric OermannFebruary 20, 2019
Combination of Active Transfer Learning and Natural Language Processing to Improve Liver Volumetry Using Surrogate Metrics with Deep Learning - RADIOLOGY ARTIFICIAL INTELLIGENCE
Eric OermannJanuary 30, 2019
Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion - SPINE
Eric OermannDecember 23, 2018
Wide and deep volumetric residual networks for volumetric image classification - ARXIV
Eric OermannDecember 23, 2018
Detecting insertion, substitution, and deletion errors in radiology reports using neural sequence-to-sequence models - ANNALS OF TRANSLATIONAL MEDICINE
Eric OermannDecember 23, 2018
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study - PLOS MEDICINE
Eric OermannDecember 23, 2018
Quantitative Computed Tomography Ventriculography for Assessment and Monitoring of Hydrocephalus - WORLD NEUROSURGERY
Eric OermannDecember 23, 2018
Natural language based machine learning models for the annotation of clinical radiology reports - RADIOLOGY
Eric OermannDecember 23, 2018
Automated deep-neural-network surveillance of cranial images for acute neurologic events - NATURE MEDICINE
PublicationsEric OermannDecember 23, 2018Radiology, Weakly supervised learning
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