Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07539 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911100800335872 |
|---|---|
| author | Shigeyasu, Yuki Harada, Shota Yoshizawa, Akihiko Terada, Kazuhiro Nakazima, Naoki Kurata, Mariyo Abe, Hiroyuki Ushiku, Tetsuo Bise, Ryoma |
| author_facet | Shigeyasu, Yuki Harada, Shota Yoshizawa, Akihiko Terada, Kazuhiro Nakazima, Naoki Kurata, Mariyo Abe, Hiroyuki Ushiku, Tetsuo Bise, Ryoma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning Shigeyasu, Yuki Harada, Shota Yoshizawa, Akihiko Terada, Kazuhiro Nakazima, Naoki Kurata, Mariyo Abe, Hiroyuki Ushiku, Tetsuo Bise, Ryoma Computer Vision and Pattern Recognition 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. |
| title | Domain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive Learning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.07539 |