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Deceptive learning in histopathology

Sahar Shahamatdar, Daryoush Saeed-Vafa, Drew Linsley, Farah Khalil, Katherine Lovinger, Lester Li, Howard McLeod, Sohini Ramachandran, Thomas Serre
doi: https://doi.org/10.1101/2022.04.21.489110
Sahar Shahamatdar
1Center for Computational Molecular Biology, Brown University, Providence, RI, USA
2Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
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Daryoush Saeed-Vafa
3Department of Anatomic Pathology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL, USA
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Drew Linsley
4Carney Institute for Brain Science, Brown University, Providence, RI, USA
5Department of Cognitive Linguistic & Psychological Sciences, Brown University, Providence, RI, USA
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  • For correspondence: drew_linsley@brown.edu
Farah Khalil
3Department of Anatomic Pathology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL, USA
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Katherine Lovinger
6Department of Molecular Biology, H. Lee Moffitt Cancer and Research Institute, Tampa, FL, USA
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Lester Li
7University of Rochester, Rochester, NY, USA
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Howard McLeod
8Geriatric Oncology Consortium, Tampa, FL, USA
9Taneja College of Pharmacy, University of South Florida, Tampa, FL, USA
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Sohini Ramachandran
1Center for Computational Molecular Biology, Brown University, Providence, RI, USA
2Department of Ecology and Evolutionary Biology, Brown University, Providence, RI, USA
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Thomas Serre
4Carney Institute for Brain Science, Brown University, Providence, RI, USA
5Department of Cognitive Linguistic & Psychological Sciences, Brown University, Providence, RI, USA
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Abstract

Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists, and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the extent to which the visual strategies learned by deep learning models in histopathological analysis are trustworthy or not has yet to be systematically analyzed. In this work, we address this problem and discover new limits on the histopathological tasks for which deep learning models learn trustworthy versus deceptive solutions. While tasks that have been extensively studied in the field like tumor detection are reliable and trustworthy, recent advances demonstrating the ability to learn molecular profiling from hematoxylin and eosin (H&E) stained slides do not hold up to closer scrutiny. Our analysis framework represents a new approach in understanding the capabilities of deep learning models, which should be incorporated into the computational pathologists toolkit.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 22, 2022.
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Deceptive learning in histopathology
Sahar Shahamatdar, Daryoush Saeed-Vafa, Drew Linsley, Farah Khalil, Katherine Lovinger, Lester Li, Howard McLeod, Sohini Ramachandran, Thomas Serre
bioRxiv 2022.04.21.489110; doi: https://doi.org/10.1101/2022.04.21.489110
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Deceptive learning in histopathology
Sahar Shahamatdar, Daryoush Saeed-Vafa, Drew Linsley, Farah Khalil, Katherine Lovinger, Lester Li, Howard McLeod, Sohini Ramachandran, Thomas Serre
bioRxiv 2022.04.21.489110; doi: https://doi.org/10.1101/2022.04.21.489110

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