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

Abbet, Christian; Studer, Linda; Fischer, Andreas; Dawson, Heather; Zlobec, Inti; Bozorgtabar, Behzad; Thiran, Jean-Philippe (2022). Self-rule to multi-adapt: Generalized multi-source feature learning using unsupervised domain adaptation for colorectal cancer tissue detection. Medical image analysis, 79(102473), p. 102473. Elsevier 10.1016/j.media.2022.102473

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Service Sector > Institute of Pathology

UniBE Contributor:

Abbet, Christian; Studer, Linda; Dawson, Heather and Zlobec, Inti

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1361-8415

Publisher:

Elsevier

Language:

English

Submitter:

Pubmed Import

Date Deposited:

17 May 2022 14:38

Last Modified:

02 Jun 2022 00:16

Publisher DOI:

10.1016/j.media.2022.102473

PubMed ID:

35576822

Uncontrolled Keywords:

Colorectal cancer Computational pathology Self-supervised learning Unsupervised domain adaptation

BORIS DOI:

10.48350/170071

URI:

https://boris.unibe.ch/id/eprint/170071

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