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Deep learning-enabled medical computer vision

delete2021-01-08
delete578
delete
OA
AI
A
Andre Esteva *
K
Katherine Chou
S
Serena Yeung
N
Nikhil Naik
A
Ali Madani
A
Ali Mottaghi
Y
Yun Liu
E
Eric J. Topol
J
Jeff Dean
R
Richard Socher
DOI：10.1038/s41746-020-00376-2delete
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Abstract

Abstract

En 中文
A decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields-including medicine-to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques-powered by deep learning-for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit-including cardiology, pathology, dermatology, ophthalmology-and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.
Keywords:
CONVOLUTIONAL NEURAL-NETWORK
ARTIFICIAL-INTELLIGENCE
DIABETIC-RETINOPATHY
DIGITAL MEDICINE
SKIN-CANCER
RECOGNITION
IMAGES
CLASSIFICATION
DERMATOLOGISTS
SEGMENTATION
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

npj Digital Medicine cover
npj Digital Medicine
IF:
15.1
Papers:
3.1K
Citations:
1.5W

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