Self-Rule to Adapt: Learning Generalized Features from Sparsely-Labeled Data Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue PhenotypingDownload PDF

Jan 27, 2021 (edited Apr 21, 2021)MIDL 2021 Conference SubmissionReaders: Everyone
  • Keywords: Computational pathology, self-supervised learning, few labeled data, unsupervised domain adaptation, colorectal cancer
  • TL;DR: Self-supervised learning and domain adaptation with few-labels applied to colorectal cancer tissue types classification.
  • Abstract: Supervised learning is conditioned by the availability of labeled data, which are especially expensive to acquire in the field of medical image analysis. 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 Self-Rule to Adapt (SRA) which takes advantage of self-supervised learning to perform domain adaptation and removes the burden of fully-labeled source datasets. SRA can effectively transfer the discriminative knowledge obtained from a few labeled source domain 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. We show that our proposed method outperforms baselines across diverse domain adaptation settings and further validate our approach to our in-house clinical cohort.
  • Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
  • Source Code Url: https://github.com/christianabbet/SRA
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
  • Data Set Url: https://zenodo.org/record/1214456, https://zenodo.org/record/53169
  • Paper Type: both
  • Source Latex: zip
  • Primary Subject Area: Unsupervised Learning and Representation Learning
  • Secondary Subject Area: Application: Histopathology
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