Open AccessPosted Content
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
Pranav Rajpurkar,Jeremy Irvin,Kaylie Zhu,Brandon Yang,Hershel Mehta,Tony Duan,Daisy Yi Ding,Aarti Bagul,Curtis P. Langlotz,Katie Shpanskaya,Matthew P. Lungren,Andrew Y. Ng +11 more
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.Abstract:
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.read more
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Automated detection of COVID-19 cases using deep neural networks with X-ray images.
Tülin Öztürk,Muhammed Talo,Eylul Azra Yildirim,Ulas Baran Baloglu,Ozal Yildirim,U. Rajendra Acharya +5 more
TL;DR: A new model for automatic COVID-19 detection using raw chest X-ray images is presented and can be employed to assist radiologists in validating their initial screening, and can also be employed via cloud to immediately screen patients.
Journal ArticleDOI
Artificial Intelligence in Healthcare
TL;DR: Recent breakthroughs in AI technologies and their biomedical applications are outlined, the challenges for further progress in medical AI systems are identified, and the economic, legal and social implications of AI in healthcare are summarized.
Journal ArticleDOI
CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin,Pranav Rajpurkar,Michael Ko,Yifan Yu,Silviana Ciurea-Ilcus,Christopher G. Chute,Henrik Marklund,Behzad Haghgoo,Robyn L. Ball,Katie Shpanskaya,Jayne Seekins,David A. Mong,Safwan Halabi,Jesse K. Sandberg,Ricky Jones,David B. Larson,Curtis P. Langlotz,Bhavik N. Patel,Matthew P. Lungren,Andrew Y. Ng +19 more
TL;DR: CheXpert as discussed by the authors is a large dataset of chest radiographs of 65,240 patients annotated by 3 board-certified radiologists with 14 observations in radiology reports, capturing uncertainties inherent in radiograph interpretation.
Journal ArticleDOI
Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.
TL;DR: Pneumonia-screening CNNs robustly identified hospital system and department within a hospital, which can have large differences in disease burden and may confound predictions.
Journal ArticleDOI
Deep Learning Applications in Medical Image Analysis
TL;DR: This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field, covering key research areas and applications of medical image classification, localization, detection, segmentation, and registration.
References
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Proceedings Article
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Diederik P. Kingma,Jimmy Ba +1 more
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Proceedings ArticleDOI
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Proceedings Article
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.
Posted Content
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe,Christian Szegedy +1 more
TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.