Majority voting of doctors improves appropriateness of AI reliance in pathology-Reference-Cited by-同舟云学术

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Majority voting of doctors improves appropriateness of AI reliance in pathology

Published:2024-10 Issue: Volume:190 Page:103315
ISSN:1071-5819
Container-title:International Journal of Human-Computer Studies
language:en
Short-container-title:International Journal of Human-Computer Studies

Author:

Gu Hongyan^ORCID,Yang Chunxu^ORCID,Magaki Shino^ORCID,Zarrin-Khameh Neda,Lakis Nelli S.,Cobos Inma,Khanlou Negar^ORCID,Zhang Xinhai R.^ORCID,Assi Jasmeet,Byers Joshua T.^ORCID,Hamza Ameer,Han Karam,Meyer Anders^ORCID,Mirbaha Hilda,Mohila Carrie A.,Stevens Todd M.,Stone Sara L.,Yan Wenzhong^ORCID,Haeri Mohammad^ORCID,Chen Xiang ‘Anthony’

Funder

National Science Foundation

The University of Kansas

Publisher

Elsevier BV

Reference88 articles.

1. NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer;Amgad;GigaScience,2022

2. A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research;Aubreville;Sci. Data,2020

3. Quantifying the scanner-induced domain gap in mitosis detection;Aubreville,2021

4. Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks;Ayhan,2018

5. Assessment of deep learning assistance for the pathological diagnosis of gastric cancer;Ba;Mod. Pathol.,2022

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