Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning
Daniel S. Kermany,Daniel S. Kermany,Michael H. Goldbaum,Wenjia Cai,Carolina C. S. Valentim,Huiying Liang,Sally L. Baxter,Alex McKeown,Ge Yang,Xiaokang Wu,Fangbing Yan,Justin Dong,Made K. Prasadha,Jacqueline Pei,Jacqueline Pei,Magdalene Yin Lin Ting,Jie Zhu,Christina Li,Sierra Hewett,Sierra Hewett,Jason Dong,Ian Ziyar,Alexander Shi,Runze Zhang,Lianghong Zheng,Rui Hou,William Shi,Xin Fu,Xin Fu,Yaou Duan,Viet Anh Nguyen Huu,Viet Anh Nguyen Huu,Cindy Wen,Edward Zhang,Edward Zhang,Charlotte Zhang,Charlotte Zhang,Oulan Li,Oulan Li,Xiaobo Wang,Michael A Singer,Xiaodong Sun,Jie Xu,Ali Tafreshi,M. Anthony Lewis,Huimin Xia,Kang Zhang +46 more
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TLDR
A diagnostic tool based on a deep-learning framework for the screening of patients with common treatable blinding retinal diseases, which demonstrates performance comparable to that of human experts in classifying age-related macular degeneration and diabetic macular edema.About:
This article is published in Cell.The article was published on 2018-02-22 and is currently open access. It has received 2750 citations till now. The article focuses on the topics: Medical diagnosis.read more
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High-performance medicine: the convergence of human and artificial intelligence
TL;DR: Over time, marked improvements in accuracy, productivity, and workflow will likely be actualized, but whether that will be used to improve the patient–doctor relationship or facilitate its erosion remains to be seen.
Journal ArticleDOI
A guide to deep learning in healthcare.
Andre Esteva,Alexandre Robicquet,Bharath Ramsundar,Volodymyr Kuleshov,Mark A. DePristo,Katherine Chou,Claire Cui,Greg S. Corrado,Sebastian Thrun,Jeffrey Dean +9 more
TL;DR: How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.
Journal ArticleDOI
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks.
TL;DR: The results suggest that Deep Learning with X-ray imaging may extract significant biomarkers related to the Covid-19 disease, while the best accuracy, sensitivity, and specificity obtained is 96.78%, 98.66%, and 96.46% respectively.
Journal ArticleDOI
Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.
Lin Li,Lixin Qin,Zeguo Xu,Youbing Yin,Xin Wang,Bin Kong,Junjie Bai,Lu Yi,Zhenghan Fang,Qi Song,Kunlin Cao,Daliang Liu,Guisheng Wang,Qi-Zhong Xu,Xisheng Fang,Shiqin Zhang,Juan Xia,Jun Xia +17 more
TL;DR: A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions.
Journal ArticleDOI
Can AI Help in Screening Viral and COVID-19 Pneumonia?
Muhammad E. H. Chowdhury,Tawsifur Rahman,Amith Khandakar,Rashid Mazhar,Muhammad Abdul Kadir,Zaid Bin Mahbub,Khandakar Reajul Islam,Muhammad Salman Khan,Atif Iqbal,Nasser Al Emadi,Mamun Bin Ibne Reaz,Mohammad Tariqul Islam +11 more
TL;DR: The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy.
References
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ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
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Rethinking the Inception Architecture for Computer Vision
TL;DR: This work is exploring ways to scale up networks in ways that aim at utilizing the added computation as efficiently as possible by suitably factorized convolutions and aggressive regularization.
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Visualizing and Understanding Convolutional Networks
Matthew D. Zeiler,Rob Fergus +1 more
TL;DR: A novel visualization technique is introduced that gives insight into the function of intermediate feature layers and the operation of the classifier in large Convolutional Network models, used in a diagnostic role to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark.
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Varun Gulshan,Lily Peng,Marc Coram,Martin C. Stumpe,Derek Wu,Arunachalam Narayanaswamy,Subhashini Venugopalan,Kasumi Widner,Tom Madams,Jorge Cuadros,Ramasamy Kim,Rajiv Raman,Philip C. Nelson,Jessica L. Mega,Dale R. Webster +14 more
TL;DR: An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.