Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning

Yuki Shigeyasu; Shota Harada; Akihiko Yoshizawa; Kazuhiro Terada; Naoki Nakazima; Mariyo Kurata; Hiroyuki Abe; Tetsuo Ushiku; Ryoma Bise

arXiv · 2508.07539

Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning

Yuki Shigeyasu·Shota Harada·Akihiko Yoshizawa·Kazuhiro Terada·Naoki Nakazima·Mariyo Kurata·Hiroyuki Abe·Tetsuo Ushiku·Ryoma Bise

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Abstract

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 than shifts between hospitals. Traditional approaches rely on multi-hospital data, but data collection challenges often make this impractical. Therefore, the proposed domain generalization method captures and leverages intra-hospital domain shifts by clustering WSI-level features from non-tumor regions and treating these clusters as domains. To mitigate domain shift, we apply contrastive learning to reduce feature gaps between WSI pairs from different clusters. The proposed method introduces a two-stage contrastive learning approach WSI-level and patch-level contrastive learning to minimize these gaps effectively.

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Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Naoki Nakazima, Mariyo Kurata, Hiroyuki Abe, Tetsuo Ushiku, Ryoma Bise. 2025-08-11. Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning. https://arxiv.org/abs/2508.07539

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