← 研究テーマ一覧
Clinical image analysis

病理・内視鏡の臨床画像解析

病理像や内視鏡像から疾患の所見と重症度を推定し、臨床の判断を支えます。

Selected Papers

代表論文

内視鏡画像を患者単位で集約して重症度を推定する図
Fig. 2 · 重症度推定に使う特徴の集約 · 出典：論文PDF ↗
Selected WACV 2025 · 内視鏡重症度

Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer

Selected Modern Pathology. 37, 6, 100485, 2024. (IF:8.209)

A Deep Learning?Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non?Small Cell Lung Carcinoma: Does it Help Pathologists Score?

Hiroaki Ito, Akihiko Yoshizawa, Kazuhiro Terada, Akiyoshi Nakakura, Mariyo Rokutan-Kurata, Tatsuhiko Sugimoto, Kazuya Nishimura, Naoki Nakajima, Shinji Sumiyoshi, Masatsugu Hamaji, Toshi Menju, Hiroshi Date, Satoshi Morita, Ryoma Bise, Hironori Haga

Selected Modern Pathology, 2024 (Top Journal on Pathology, IF:8.209)

Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non?small Cell Lung Carcinomas With Histologic Images

Kazuhiro Terada, Akihiko Yoshizawa, Xiaoqing Liu, Hiroaki Ito, Masatsugu Hamaji, Toshi Menju, Hiroshi Date, Ryoma Bise, and Hironori Haga

Fig. 2 · 潰瘍性大腸炎画像の順序情報
Fig. 2 · 潰瘍性大腸炎画像の順序情報 · 出典：論文PDF ↗
Selected International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (ac

Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels

Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka and Seiichi Uchida

内視鏡画像の重症度の順序を利用し、限られた正解から分類します。

論文を見る ↗
Fig. 3 · 複数倍率の病理画像を統合
Fig. 3 · 複数倍率の病理画像を統合 · 出典：論文PDF ↗
Selected IEEE CVPR, 2019. (Top Conference in Computer Vision, Poster, acceptance rate:25%) pdf

Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology

H. Tokunaga, Y. Teramoto, A. Yoshizawa, R. Bise

複数の視野と倍率から得た特徴を適応的に統合し、病理画像を分割します。

論文を見る ↗
Publications

関連論文

  1. Weakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary Labels
    Nao Sugeta, Kaito Shiku, Shinnosuke Matsuo, and Ryoma Bise · The Fifth Workshop on Applications of Medical AI (AMAI), MICCAI 2026 Workshop.
  2. Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models
    Masashi Yamana, Ryoma Bise, Makoto Endo, Tomoya Matsunobu, Nokitaka Setsu, Nobuhiko Yokoyama, Yasuharu Nakashima, Seiichi Uchida · Bone & Joint Research, 14(9), 760, 2025.
  3. Enhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection Module
    Xiaotong Ji, Ryoma Bise, Seiichi Uchida · International Conference on Machine Vision Applications (MVA), 2025
  4. Ordinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated Transformer
    Kaito Shiku, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka, and Ryoma Bise · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025. (accepted, acceptance rate in Round 1:12% (167/1381) )
  5. A Deep Learning?Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non?Small Cell Lung Carcinoma: Does it Help Pathologists Score?
    Hiroaki Ito, Akihiko Yoshizawa, Kazuhiro Terada, Akiyoshi Nakakura, Mariyo Rokutan-Kurata, Tatsuhiko Sugimoto, Kazuya Nishimura, Naoki Nakajima, Shinji Sumiyoshi, Masatsugu Hamaji, Toshi Menju, Hiroshi Date, Satoshi Morita, Ryoma Bise, Hironori Haga · Modern Pathology. 37, 6, 100485, 2024. (IF:8.209)
  6. Development of an automatic surgical planning system for high tibial osteotomy using artificial intelligence
    Kazuki Miyama, Takenori Akiyama, Ryoma Bise, Shunsuke Nakamura, Yasuharu Nakashima, and Seiichi Uchida · Knee. 48, pp.128-137, 2024. (IF:1.9)
  7. Viable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcoma
    Kengo Kawaguchi, Kazuki Miyama, Makoto Endo, Ryoma Bise, Kenichi Kohashi, Takeshi Hirose, Akira Nabeshima, Toshifumi Fujiwara, Yoshihiro Matsumoto, Yoshinao Oda, and Yasuharu Nakashima · npj Precision Oncology, 2024 (Top Journal on Oncology, IF:10.123)
  8. Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non?small Cell Lung Carcinomas With Histologic Images
    Kazuhiro Terada, Akihiko Yoshizawa, Xiaoqing Liu, Hiroaki Ito, Masatsugu Hamaji, Toshi Menju, Hiroshi Date, Ryoma Bise, and Hironori Haga · Modern Pathology, 2024 (Top Journal on Pathology, IF:8.209)
  9. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale
    Kaoru Takabayashi, Taku Kobayashi, Katsuyoshi Matsuoka, Barrett G Levesque, Takuji Kawamura, Kiyohito Tanaka, Takeaki Kadota, Ryoma Bise, Seiichi Uchida, Takanori Kanai, and Haruhiko Ogata · Digestive Endoscopy, 32, 9, pp.1402-1411, 2023 (IF:6.337)
  10. Deep learning-based automatic-bone-destruction-evaluation system using contextual information from other joints
    Kazuki Miyama, Ryoma Bise, Satoshi Ikemura, Kazuhiro Kai, Masaya Kanahori, Shinkichi Arisumi, Taisuke Uchida, Yasuharu Nakashima, and Seiichi Uchida · Arthritis Research & Therapy, 2022. (IF:5.606)
  11. Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
    K Araki, M Rokutan-Kurata, K Terada, A Yoshizawa, R Bise · International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
  12. Order-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited Labels
    Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka and Seiichi Uchida · International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (accepted, top conference in medial image analysis)
  13. Endoscopic Image Clustering with Temporal Ordering Information Based on Dynamic Programming
    S. Harada, H. Hayashi, R. Bise, K. Tanaka, Q. Meng, and S. Uchida · Proceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019.
  14. Adaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in Pathology
    H. Tokunaga, Y. Teramoto, A. Yoshizawa, R. Bise · IEEE CVPR, 2019. (Top Conference in Computer Vision, Poster, acceptance rate:25%) pdf
  15. Quantitative assessment of fibroelastosis reveals distinct elastic and collagen fibre patterns in idiopathic and secondary pleuroparenchymal fibroelastosis
    Hiroyuki Katsuragawa, Hiroaki Ito, Tomohiro Handa, Masatsugu Hamaji, Toshi Menju, Ryo Sakamoto, Ryoma Bise, Hiroshi Date, and Hironori Haga · Virchows Archiv (in press).

Publication一覧を見る →