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
TLDR
How these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems are described.Abstract:
Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.read more
Citations
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Journal ArticleDOI
Applications of machine learning in drug discovery and development.
Jessica Vamathevan,Dominic Clark,Paul Czodrowski,Ian Dunham,Edgardo Ferran,George Lee,Bin Li,Anant Madabhushi,Anant Madabhushi,Parantu K. Shah,Michaela Spitzer,Shanrong Zhao +11 more
TL;DR: The most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development are discussed and major hurdles in the field are highlighted.
Journal ArticleDOI
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.
Journal ArticleDOI
Key challenges for delivering clinical impact with artificial intelligence.
TL;DR: The safe and timely translation of AI research into clinically validated and appropriately regulated systems that can benefit everyone is challenging, and robust clinical evaluation, using metrics that are intuitive to clinicians and ideally go beyond measures of technical accuracy, is essential.
Journal ArticleDOI
Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.
Kang Zhang,Xiaohong Liu,Jun Shen,Zhihuan Li,Ye Sang,Xingwang Wu,Yunfei Zha,Wenhua Liang,Chengdi Wang,Ke Wang,Linsen Ye,Ming Gao,Zhongguo Zhou,Liang Li,Jin Wang,Zehong Yang,Huimin Cai,Jie Xu,Lei Yang,Wenjia Cai,Wenqin Xu,Shaoxu Wu,Wei Zhang,Shanping Jiang,Lianghong Zheng,Xuan Zhang,Li Wang,Liu Lu,Jiaming Li,Haiping Yin,Winston Wang,Oulan Li,Charlotte Zhang,Liang Liang,Tao Wu,Ruiyun Deng,Kang Wei,Yong Zhou,Ting Chen,Johnson Y.N. Lau,Manson Fok,Jianxing He,Tianxin Lin,Weimin Li,Guangyu Wang +44 more
TL;DR: Using a large computed Tomography database from 4,154 patients, an AI system is developed that can diagnose NCP and differentiate it from other common pneumonia and normal controls and is made available globally to assist the clinicians to combat COVID-19.
Journal ArticleDOI
Toward Causal Representation Learning
Bernhard Schölkopf,Francesco Locatello,Stefan Bauer,Nan Rosemary Ke,Nal Kalchbrenner,Anirudh Goyal,Yoshua Bengio +6 more
TL;DR: The authors reviewed fundamental concepts of causal inference and related them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Journal ArticleDOI
Generative Adversarial Nets
Ian Goodfellow,Jean Pouget-Abadie,Mehdi Mirza,Bing Xu,David Warde-Farley,Sherjil Ozair,Aaron Courville,Yoshua Bengio +7 more
TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.