Self-Rule to Multi-Adapt: Generalized Multi-source Feature Learning Using Unsupervised Domain Adaptation for Colorectal Cancer Tissue Detection. (arXiv:2108.09178v2 [cs.CV] UPDATED)

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 …

arxiv cancer cv detection domain adaptation learning unsupervised