Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection.

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State: Public
Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_A4C8C9498B40
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection.
Journal
Medical image analysis
Author(s)
Abbet C., Studer L., Fischer A., Dawson H., Zlobec I., Bozorgtabar B., Thiran J.P.
ISSN
1361-8423 (Electronic)
ISSN-L
1361-8415
Publication state
Published
Issued date
07/2022
Peer-reviewed
Oui
Volume
79
Pages
102473
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Abstract
Supervised learning is constrained by the availability of labeled data, which are especially expensive to acquire in the field of digital pathology. Making use of open-source data for pre-training or using domain adaptation can be a way to overcome this issue. However, pre-trained networks often fail to generalize to new test domains that are not distributed identically due to tissue stainings, types, and textures variations. Additionally, current domain adaptation methods mainly rely on fully-labeled source datasets. In this work, we propose Self-Rule to Multi-Adapt (SRMA), which takes advantage of self-supervised learning to perform domain adaptation, and removes the necessity of fully-labeled source datasets. SRMA can effectively transfer the discriminative knowledge obtained from a few labeled source domain's data to a new target domain without requiring additional tissue annotations. Our method harnesses both domains' structures by capturing visual similarity with intra-domain and cross-domain self-supervision. Moreover, we present a generalized formulation of our approach that allows the framework to learn from multiple source domains. We show that our proposed method outperforms baselines for domain adaptation of colorectal tissue type classification in single and multi-source settings, and further validate our approach on an in-house clinical cohort. The code and trained models are available open-source: https://github.com/christianabbet/SRA.
Keywords
Colorectal Neoplasms, Humans, Colorectal cancer, Computational pathology, Self-supervised learning, Unsupervised domain adaptation
Pubmed
Web of science
Open Access
Yes
Create date
23/05/2022 12:41
Last modification date
24/10/2023 6:19
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