Hidden stratification causes clinically meaningful failures in machine learning for medical imaging-Reference-Cited by-同舟云学术

Hidden stratification causes clinically meaningful failures in machine learning for medical imaging

Published:2020-04-02 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Conference on Health, Inference, and Learning
language:
Short-container-title:

Author:

Oakden-Rayner Luke¹,Dunnmon Jared²,Carneiro Gustavo¹,Re Christopher²

Affiliation:

1. Australian Institute for Machine Learning, University of Adelaide

2. Department of Computer Science, Stanford University

Funder

Google

Toshiba

Teradata

Ant Financial

Intel Corporation

NEC Corporation

Taiwan Semiconductor Manufacturing Company

Accenture

Qualcomm

Analog Devices

Facebook

VMWare

Infosys

National Institutes of Health

NXP

Xilinx

LETI-CEA

American Family Insurance

Swiss Re

Intelligence Community Postdoctoral Research Fellowship Program

National Science Foundation

IBM

Hitachi

Okawa Foundation for Information and Telecommunications

Google Cloud

Office of Naval Research

Gordon and Betty Moore Foundation

ARM

Defense Advanced Research Projects Agency

Microsoft

BASF

Ericsson

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3368555.3384468

Reference41 articles.

1. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet

2. A dendrite method for cluster analysis

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