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SQuAD: 100,000+ Questions for Machine Comprehension of Text
TLDR
A strong logistic regression model is built, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). Expand
Know What You Don’t Know: Unanswerable Questions for SQuAD
TLDR
SQuadRUn is a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. Expand
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
TLDR
An algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists is developed, and it is found that CheXNet exceeds average radiologist performance on the F1 metric. Expand
CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison
TLDR
A labeler is designed to automatically detect the presence of 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation, in CheXpert, a large dataset that contains 224,316 chest radiographs of 65,240 patients. Expand
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
TLDR
It is demonstrated that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. Expand
Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks
We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearableExpand
MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs.
TLDR
MURA, a large dataset of musculoskeletal radiographs containing 40,561 images from 14,863 studies, where each study is manually labeled by radiologists as either normal or abnormal is introduced, and a 169-layer DenseNet baseline model is trained to detect and localize abnormalities. Expand
Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
TLDR
CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs, achieved radiologist-level performance on 11 pathologies and did not achieve radiologists' level performance on 3 pathologies. Expand
An Empirical Evaluation of Deep Learning on Highway Driving
TLDR
It is shown how existing convolutional neural networks can be used to perform lane and vehicle detection while running at frame rates required for a real-time system, lending credence to the hypothesis that deep learning holds promise for autonomous driving. Expand
Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
TLDR
A deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams is developed and the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation is supported. Expand
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