Self-Rule to Adapt: Generalized Multi-source Feature Learning Using
Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
- URL: http://arxiv.org/abs/2108.09178v1
- Date: Fri, 20 Aug 2021 13:52:33 GMT
- Title: Self-Rule to Adapt: Generalized Multi-source Feature Learning Using
Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection
- Authors: Christian Abbet, Linda Studer, Andreas Fischer, Heather Dawson, Inti
Zlobec, Behzad Bozorgtabar, Jean-Philippe Thiran
- Abstract summary: Supervised learning is constrained by the availability of labeled data.
We propose SRA, which takes advantage of self-supervised learning to perform domain adaptation.
- Score: 9.074125289002911
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- 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
variations in tissue stainings, types, and textures. Additionally, current
domain adaptation methods mainly rely on fully-labeled source datasets. In this
work, we propose SRA, which takes advantage of self-supervised learning to
perform domain adaptation and removes the necessity of a fully-labeled source
dataset. SRA 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 architecture to learn from multi-source domains. We
show that our proposed method outperforms baselines for domain adaptation of
colorectal tissue type classification and further validate our approach on our
in-house clinical cohort. The code and models are available open-source:
https://github.com/christianabbet/SRA.
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