4 papers
Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning
Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa +6
In this paper, we address domain shifts in pathological images by focusing on shifts within whole slide images~(WSIs), such as patient characteristics and tissue thickness, rather…
Theoretical Proportion Label Perturbation for Learning from Label Proportions in Large Bags
Shunsuke Kubo, Shinnosuke Matsuo, Daiki Suehiro +4
Learning from label proportions (LLP) is a kind of weakly supervised learning that trains an instance-level classifier from label proportions of bags, which consist of sets of inst…
Learning from Partial Label Proportions for Whole Slide Image Segmentation
Shinnosuke Matsuo, Daiki Suehiro, Seiichi Uchida +4
In this paper, we address the segmentation of tumor subtypes in whole slide images (WSI) by utilizing incomplete label proportions. Specifically, we utilize `partial' label proport…
Proportion Estimation by Masked Learning from Label Proportion
Takumi Okuo, Kazuya Nishimura, Hiroaki Ito +3
The PD-L1 rate, the number of PD-L1 positive tumor cells over the total number of all tumor cells, is an important metric for immunotherapy. This metric is recorded as diagnostic i…