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Domain adaptation & generalization

Medical AI under domain shift

We study domain adaptation and generalization so that medical image models remain useful across hospitals, scanners, and specimen conditions.

Selected Papers

Selected papers

Ranking-based alignment of source and target domains
Fig. 1 · Ranking-guided domain adaptation · Source: paper PDF ↗
ISBI 2026 · Semi-supervised adaptation · Severity ranking

Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification

Ranking relationships guide severity estimation when endoscopic images differ across sites.

Fig. 2 · Heatmap-based pseudo-labeling
Fig. 2 · Heatmap-based pseudo-labeling · Source: paper PDF ↗
Selected Medical Image Analysis 2022 · Unsupervised domain adaptation

Effective pseudo-labeling based on heatmap for unsupervised domain adaptation in cell detection

Pseudo cell-position heatmaps help adapt cell detectors across imaging conditions.

Paper and abstract ↗
Fig. 2 · Iterative cell-position heatmap learning
Fig. 2 · Iterative cell-position heatmap learning · Source: paper PDF ↗
MICCAI 2021 · Cell detection under domain shift

Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap

Pseudo cell-position heatmaps and iterative learning address shifts between source and target cell images.

Paper and abstract ↗
Publications

Related publications

  1. Ranking-Guided Semi-Supervised Domain Adaptation for Severity Classification
    Shota Harada, Ryoma Bise, Kiyohito Tanaka, and Seiichi Uchida · IEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026.
  2. Domain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level Diagnoses
    T Yamaguchi, BK Iwana, R Bise, S Harada, T Okuo, K Tanaka, K Shiku · Workshop on MICCAI: MLMI, 2025.
  3. 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 · International Conference on Machine Vision Applications (MVA), 2025
  4. Weakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-Labeling
    Takumi Okuo, Shinnosuke Matsuo, Shota Harada, Kiyohito Tanaka, and Ryoma Bise · International Joint Conference on Neural Networks (IJCNN), 2025
  5. Domain Generalization for Pathological Images Using the Storage Period Information
    Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Naoki Nakazima, Mariyo Kurata, Hiroyuki Abe, Tetsuo Ushiku, and Ryoma Bise · IEEE International Symposium on Biomedical Imaging (ISBI), 2024.
  6. Cluster Entropy: Active Domain Adaptation in Pathological Image Segmentation
    Xiaoqing Liu, Kengo Araki, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, and Ryoma Bise · IEEE International Symposium on Biomedical Imaging (ISBI), 2023. (Oral)
  7. Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification
    Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, and Seiichi Uchida · IEEE International Symposium on Biomedical Imaging (ISBI), 2023.
  8. Effective pseudo-labeling based on heatmap for unsupervised domain adaptation in cell detection
    Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, and Ryoma Bise · Medical Image Analysis, vol.79, 102436, https://doi.org/10.1016/j.media.2022.102436, 2022 (top journal in medicalImage analysis, IF:13.828)
  9. Cell Detection in Domain Shift Problem Using Pseudo-Cell-Position Heatmap
    Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, and Ryoma Bise · International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (accepted, top conference in medial image analysis, Provisional acceptanc

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