Academic work · Ryoma Bise

Publications

Peer-reviewed papers, conference presentations, patents, and research grants.

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Ryoma Bise · Publications

Peer-reviewed papers

108 items
202614 papers
  1. Kaito Shiku, Kazuya Nishimura, Yasuhiro Kojima, and Ryoma BisePreserving DEG Rankings for Gene Discovery in Histology-Based Spatial Gene Expression PredictionThe Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2026.
  2. Shumpei Takezaki, Duway Nicolas Lesmes-Leon, Ryoma Bise, Gillian Lovell, Bianca Migliori, Andreas Dengel, and Sheraz AhmedRethinking Microscopy Generation: Co-Designed Diffusion for Biologically Interpretable Single-Cell SynthesisBritish Machine Vision Conference (BMVC), 2026.
  3. Phuong Ngoc Nguyen, Kaito Shiku, Ryoma Bise, Seiichi Uchida, and Shinnosuke MatsuoLeveraging Vision-Language Models as Weak Annotators in Active LearningIEEE International Conference on Image Processing (ICIP), 2026. (Spotlight Oral; top 8% of accepted papers)
  4. Nao Sugeta, Kaito Shiku, Shinnosuke Matsuo, and Ryoma BiseWeakly Supervised Instance-Level Gleason Pattern Estimation Using Primary and Secondary LabelsThe Fifth Workshop on Applications of Medical AI (AMAI), MICCAI 2026 Workshop.
  5. Kazuya Nishimura, Ryoma Bise, Haruka Hirose, and Yasuhiro KojimaLeveraging Cell-level Spatial Transcriptomics as Weak Supervision for Nuclei ClassificationInternational Workshop on Medical Imaging Analysis for Spatial Omics (MISO), MICCAI 2026 Workshop.
  6. Hayato Inoue, Shota Harada, Shumpei Takezaki, and Ryoma BiseCell Instance Segmentation via Multi-Task Image-to-Image Schrödinger BridgeInternational Joint Conference on Neural Networks (IJCNN), 2026.
  7. Kohki Akiba, Shinnosuke Matsuo, Shota Harada, and Ryoma BiseLeveraging Label Proportion Prior for Class-Imbalanced Semi-Supervised LearningIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2026.
  8. Hiroyuki Katsuragawa, Hiroaki Ito, Tomohiro Handa, Masatsugu Hamaji, Toshi Menju, Ryo Sakamoto, Ryoma Bise, Hiroshi Date, and Hironori HagaQuantitative assessment of fibroelastosis reveals distinct elastic and collagen fibre patterns in idiopathic and secondary pleuroparenchymal fibroelastosisVirchows Archiv (in press).
  9. Kazuya Nishimura, Ryoma Bise, Shinnosuke Matsuo, Haruka Hirose, and Yasuhiro KojimaCell-Type Prototype-Informed Neural Network for Gene Expression Estimation from Pathology ImagesThe IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026.
  10. Soichi Mita, Shumpei Takezaki, and Ryoma BiseVesselFusion: Diffusion Models for Vessel Centerline Extraction from 3D CT ImagesIEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026.
  11. Masashi Tahara, Kazuya Nishimura, Shumpei Takezaki, and Ryoma BiseBridging the Density Gap: Diffusion Model for Stepwise Generation of Dense Cell Images from Sparse DataIEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026. (Best Paper Runners-Up Award)
  12. Kaito Shiku, Ichika Seo, Tetsuya Matoba, Rissei Hino, Yasuhiro Nakano, and Ryoma BiseHypernetwork-Based Adaptive Aggregation for Multimodal Multiple-Instance Learning in Predicting Coronary Calcium DebulkingIEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026.
  13. Shota Harada, Ryoma Bise, Kiyohito Tanaka, and Seiichi UchidaRanking-Guided Semi-Supervised Domain Adaptation for Severity ClassificationIEEE 23rd International Symposium on Biomedical Imaging (ISBI), 2026.
  14. Kaito Shiku, Kazuya Nishimura, Shinnosuke Matsuo, Yasuhiro Kojima, and Ryoma BiseAuxiliary Gene Learning: Spatial Gene Expression Estimation by Auxiliary Gene SelectionThe 40th Annual AAAI Conference on Artificial Intelligence (AAAI), 2026.
202514 papers
  1. Motokazu Uchigashima, Risa Iguchi, Kazuma Fujii, Kaito Shiku, Ryoma Bise, et. al.,Single-cell synaptome mapping of endogenous protein subpopulations in mammalian brain Nature Comunications, 16, 9705, 2025.
  2. Kazuya Nishimura, Haruka Hirose, Ryoma Bise, Kaito Shiku, Yasuhiro KojimaLearning to Relative Expression under Batch Effects and Stochastic Noise in Spatial TranscriptomicsThe Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS), 2025.
  3. Kaito Shiku, Shinnosuke, Matsuo, Daiki Suehiro, and Ryoma BiseLearning from Majority Label: A Novel Problem in Multi-class Multiple-Instance LearningPattern Recognition, 112425, 2025.
  4. Masashi Yamana, Ryoma Bise, Makoto Endo, Tomoya Matsunobu, Nokitaka Setsu, Nobuhiko Yokoyama, Yasuharu Nakashima, Seiichi UchidaDevelopment and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection modelsBone & Joint Research, 14(9), 760, 2025.
  5. Shumpei Takezaki, Ryoma Bise, Shinnosuke MatsuoNoiseCutMix: A Novel Data Augmentation Approach by Mixing Estimated Noise in Diffusion Models.International Conference on Computer Vision Workshop LIMIT (ICCVW), 2025.
  6. Junda Liao, Chu Zhou, Yuta Asano, Yushi Suzuki, Ryoma Bise, Nobuaki Imanishi, Kazuo Kishi, Sadakazu Aiso, and Imari SatoVascular Photoacoustic Volume Registration via 2D Feature Matching with Reverse Mapping Based on Maximum Intensity ProjectionInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025.
  7. T Yamaguchi, BK Iwana, R Bise, S Harada, T Okuo, K Tanaka, K ShikuDomain Adaptation for Ulcerative Colitis Severity Estimation Using Patient-Level DiagnosesWorkshop on MICCAI: MLMI, 2025.
  8. Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Naoki Nakazima, Mariyo Kurata, Hiroyuki Abe, Tetsuo Ushiku, Ryoma BiseDomain Generalization of Pathological Image Segmentation by Patch-Level and WSI-Level Contrastive LearningInternational Conference on Machine Vision Applications (MVA), 2025
  9. Xiaotong Ji, Ryoma Bise, Seiichi UchidaEnhancing Reliability of Medical Image Diagnosis through Top-rank Learning with Rejection ModuleInternational Conference on Machine Vision Applications (MVA), 2025
  10. Takumi Okuo, Shinnosuke Matsuo, Shota Harada, Kiyohito Tanaka, and Ryoma BiseWeakly-Supervised Domain Adaptation with Proportion-Constrained Pseudo-LabelingInternational Joint Conference on Neural Networks (IJCNN), 2025
  11. Masashi Tahara, and Ryoma BiseMedical Image Alignment for Different Resolutions and Fields of View Using Contrastive Learning with Feature-Level SimilarityInternational Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),2025
  12. Shinnosuke Matsuo, Riku Togashi, Ryoma Bise, Seiichi Uchida, and Masahiro NomuraInstance-wise Supervision-level Optimization in Active LearningIEEE CVPR, 2025. (Top Conference in Computer Vision, acceptance rate:22.1%)
  13. Kazuya Nishimura, Ryoma Bise, and Yasuhiro KojimaTowards Spatial Transcriptomics-Guided Pathological Image Recognition With Batch-Agnostic EncoderIEEE International Symposium on Biomedical Imaging (ISBI), 2025.
  14. Kaito Shiku, Kazuya Nishimura, Daiki Suehiro, Kiyohito Tanaka, and Ryoma BiseOrdinal Multiple-instance Learning for Ulcerative Colitis Severity Estimation with Selective Aggregated TransformerIEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025. (accepted, acceptance rate in Round 1:12% (167/1381) )
202415 papers
  1. Biqi Chen, Zi Yin, Billy Wai-Lung Ng, Dan Michelle Wang, Rocky S Tuan, Ryoma Bise, and Dai Fei Elmer KerLabel-free live cell recognition and tracking for biological discoveries and translational applicationsnpj Imaging, 2, 41, 2024.
  2. Tatsuhiro Eguchi, Shumpei Takezaki, Mihoko Shimano, Takayuki Yagi, and Ryoma BiseGuidance-base Diffusion Models for Improving Photoacoustic Image QualityThe British Machine Vision Conference (BMVC), 2024. (accepted, acceptance rate:25.8% (264/1020) )
  3. Shunsuke Kubo, Shinnosuke Matsuo, Daiki Suehiro, Kazuhiro Terada, Hiroaki Ito, Akihiko Yoshizawa and Ryoma BiseTheoretical Proportion Label Perturbation for Learning from Label Proportions in Large BagsEuropean Conference on Artificial Intelligence (ECAI), 2024. (acceptted, acceptance rate:23% (547/2344) )
  4. Takehiro Yamane, Itaru Tsuge, Susumu Saito, and Ryoma BiseAdaptive Pseudo Label Selection for Individual Unlabeled Data by Positive and Unlabeled LearningMICCAI Workshop (ADSMI), 2024. (acceptted)
  5. Takeaki Kadota, Hideaki Hayashi, Ryoma Bise, Kiyohito Tanaka, and Seiichi UchidaDeep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis SeverityMedical Image Analysis, 2024. (accepted, IF:10.7)
  6. Shinnosuke Matsuo, Daiki Suehiro, Seiichi Uchida, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa and Ryoma BiseLearning from Partial Label Proportions for Whole Slide Image SegmentationInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI),2024. (Early Accept, acceptance rate:11%)
  7. Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, and Ryoma BiseeDiameter-based pseudo labeling for pathological image segmentationInternational Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),2024
  8. Yuki Shigeyasu, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Naoki Nakazima, Mariyo Kurata, Hiroyuki Abe, Tetsuo Ushiku, and Ryoma BiseDomain Generalization for Pathological Images Using the Storage Period InformationIEEE International Symposium on Biomedical Imaging (ISBI), 2024.
  9. 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 HagaA Deep Learning?Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non?Small Cell Lung Carcinoma: Does it Help Pathologists Score?Modern Pathology. 37, 6, 100485, 2024. (IF:8.209)
  10. Xiaoqing Liu, KenjiOno, and Ryoma BiseA data augmentation approach that ensures the reliability of foregrounds in medical image segmentationImage and Vision Computing, 147, 105056, 2024. (IF:4.2)
  11. Kazuki Miyama, Takenori Akiyama, Ryoma Bise, Shunsuke Nakamura, Yasuharu Nakashima, and Seiichi UchidaDevelopment of an automatic surgical planning system for high tibial osteotomy using artificial intelligenceKnee. 48, pp.128-137, 2024. (IF:1.9)
  12. Kosuke Tomimatsu, Takeru Fujii, Ryoma Bise, Kazufumi Hosoda, Yosuke Taniguchi, Hiroshi Ochiai, Hiroaki Ohishi, Kanta Ando, Ryoma Minami, Kaori Tanaka, Taro Tachibana, Seiichi Mori, Akihito Harada, Kazumitsu Maehara, Masao Nagasaki, Seiichi Uchida, Hiroshi Kimura, Masashi Narita, and Yasuyuki OhkawaPrecise immunofluorescence canceling for highly multiplexed imaging to capture specific cell statesNature communications. 15, 1, 3657, 2024. (IF:17.7)
  13. Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro, and Ryoma BiseCounting Network for Learning from Majority LabelIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP),2024,(Top Conference in Signal Processing)
  14. Kengo Kawaguchi, Kazuki Miyama, Makoto Endo, Ryoma Bise, Kenichi Kohashi, Takeshi Hirose, Akira Nabeshima, Toshifumi Fujiwara, Yoshihiro Matsumoto, Yoshinao Oda, and Yasuharu NakashimaViable tumor cell density after neoadjuvant chemotherapy assessed using deep learning model reflects the prognosis of osteosarcomanpj　Precision Oncology, 2024 (Top Journal on Oncology, IF:10.123)
  15. Kazuhiro Terada, Akihiko Yoshizawa, Xiaoqing Liu, Hiroaki Ito, Masatsugu Hamaji, Toshi Menju, Hiroshi Date, Ryoma Bise, and Hironori HagaDeep Learning for Predicting Effect of Neoadjuvant Therapies in Non?small Cell Lung Carcinomas With Histologic ImagesModern Pathology, 2024 (Top Journal on Pathology, IF:8.209)
202313 papers
  1. Yuki Ogura, Mihoko Shimano, Ryoma Bise, Toyonobu Yamashita, Chika Katagiri, and Imari SatoAnalysis of optical absorption of photoaged human skin using a high-frequency illumination microscopy analysis systemExperimental Dermatology, 32, 9, pp.1402-1411, 2023. (IF:3.6)
  2. Kaoru Takabayashi, Taku Kobayashi, Katsuyoshi Matsuoka, Barrett G Levesque, Takuji Kawamura, Kiyohito Tanaka, Takeaki Kadota, Ryoma Bise, Seiichi Uchida, Takanori Kanai, and Haruhiko OgataArtificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scaleDigestive Endoscopy, 32, 9, pp.1402-1411, 2023 (IF:6.337)
  3. Takanori Asanomi, Shinnosuke Matsuo, Daiki Suehiro and Ryoma BiseMixBag: Bag-Level Data Augmentation for Learning from Label ProportionsInternational Conference on Computer Vision (ICCV),2023,(top conference in computer vision)
  4. Takumi Okuo, Kazuya Nishimura, Hiroaki Ito, Kazuhiro Terada, Akihiko Yoshizawa, and Ryoma BiseProportion Estimation by Masked Learning from Label ProportionWorkshop on MICCAI: DALI, pp.117-126, 2023.
  5. Kazuya Nishimura, Ami Katanaya, Shinichiro Chuma, and Ryoma BiseMitosis Detection from Partial Annotation by Dataset Generation via Frame-Order FlippingInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2023), 2023,(top conference in medial image analysis)
  6. Kaito Shiku, Hiromitsu Shirai, Takeshi Ishihara, and Ryoma BiseCell Tracking in C. elegans with Cell Position Heatmap-Based Alignment and Pairwise DetectionInternational Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),2023
  7. Shinnosuke Matsuo, Ryoma Bise, Seiichi Uchida, and Daiki SuehiroLearning From Label Proportion with Online Pseudo-Label Decision by Regret MinimizationIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP2023),2023 (top conference in signal processing)
  8. Yuki Shigeyasu, Shota Harada, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, and Ryoma BiseSpatial Distribution-based Pseudo Labeling for Pathological Image SegmentationIEEE International Symposium on Biomedical Imaging (ISBI), 2023.
  9. Xiaoqing Liu, Kengo Araki, Shota Harada, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, and Ryoma BiseCluster Entropy: Active Domain Adaptation in Pathological Image SegmentationIEEE International Symposium on Biomedical Imaging (ISBI), 2023. (Oral)
  10. Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, and Seiichi UchidaCluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image ClassificationIEEE International Symposium on Biomedical Imaging (ISBI), 2023.
  11. Xiaoqing Liu, Kenji Ono, and Ryoma BiseMixing Data Augmentation with Preserving Foreground Regions in Medical Image SegmentationIEEE International Symposium on Biomedical Imaging (ISBI), 2023. (Oral)
  12. Takanori Asanomi, Kazuya Nishimura, and Ryoma BiseMulti-Frame Attention with Feature-Level Warping for Drone Crowd TrackingWinter Conference on Applications of Computer Vision 2023 (WACV2023),2023. (accepted)
  13. Kazuya Nishimura, and Ryoma BiseWeakly Supervised Cell-Instance Segmentation with Two Types of Weak Labels by Single Instance PastingWinter Conference on Applications of Computer Vision 2023 (WACV2023),2023. (accepted)
20225 papers
  1. Kazuki Miyama, Ryoma Bise, Satoshi Ikemura, Kazuhiro Kai, Masaya Kanahori, Shinkichi Arisumi, Taisuke Uchida, Yasuharu Nakashima, and Seiichi UchidaDeep learning-based automatic-bone-destruction-evaluation system using contextual information from other jointsArthritis Research & Therapy, 2022. (IF:5.606)
  2. T Asanomi, K Nishimura, H Song, J Hayashida, H Sekiguchi, T Yagi, I Sato, and R BiseUnsupervised Deep Robust Non-Rigid Alignment by Low-Rank Loss and Multi-Input AttentionInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2022), 2022,(top conference in medial image analysis)
  3. T Sugimoto, H Ito, Y Teramoto, A Yoshizawa and R BiseMulti-Class Cell Detection Using Modified Self-AttentionCVPR Workshop, Computer Vision for Microscopy Image Analysis (CVMI), 2022
  4. Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, and Ryoma BiseEffective pseudo-labeling based on heatmap for unsupervised domain adaptation in cell detectionMedical Image Analysis, vol.79, 102436, https://doi.org/10.1016/j.media.2022.102436, 2022 (top journal in medicalImage analysis, IF:13.828)
  5. J Hayashida, K Nishimura, R BiseConsistent Cell Tracking in Multi-Frames With Spatio-Temporal Context by Object-Level Warping LossIEEE/CVF Winter Conference on Applications of Computer Vision, pp.1727-1736, 2022
20218 papers
  1. K Nishimura, C Wang, K Watanabe, R BiseWeakly Supervised Cell Instance Segmentation Under Various ConditionsMedical Image Analysis, vol.73, 102182, https://doi.org/10.1016/j.media.2021.102182, 2021, October (top journal in medicalImage analysis, IF:11.148)
  2. K Araki, M Rokutan-Kurata, K Terada, A Yoshizawa, R BisePatch-Based Cervical Cancer Segmentation using Distance from Boundary of TissueInternational Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
  3. R Kikkawa, H Kajita, N Imanishi, S Aiso, R BiseUnsupervised Body Hair Detection by Positive-Unlabeled Learning in Photoacoustic ImageInternational Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021
  4. Kazuya Nishimura, Hyeonwoo Cho, and Ryoma BiseSemi-supervised Cell Detection in Time-lapse Images Using Temporal ConsistencyInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (accepted, top conference in medial image analysis)
  5. Kazuma Fujii, Daiki Suehiro, Kazuya Nishimura, and Ryoma BiseCell Detection from Imperfect Annotation by Pseudo Label Selection Using P-classificationInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (accepted, top conference in medial image analysis)
  6. Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka and Seiichi UchidaOrder-Guided Disentangled Representation Learning for Ulcerative Colitis Classification with Limited LabelsInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (accepted, top conference in medial image analysis)
  7. Hyeonwoo Cho, Kazuya Nishimura, Kazuhide Watanabe, and Ryoma BiseCell Detection in Domain Shift Problem Using Pseudo-Cell-Position HeatmapInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2021), 2021, (accepted, top conference in medial image analysis, Provisional acceptance rate:13%)
  8. Shota Harada, Ryoma Bise, Hideaki Hayashi, Kiyohito Tanaka, and Seiichi UchidaSoft and Self Constrained Clustering for Group-Based LabelingMedical Image Analysis, https://doi.org/10.1016/j.media.2021.102097, 2021, May (in press, top journal in medicalImage analysis, IF:11.148)
20206 papers
  1. Akiko Kondow, Kiyoshi Ohnuma, Yasuhiro Kamei, Atsushi Taniguchi, Ryoma Bise, Yoichi Sato, Hisateru Yamaguchi, Shigenori Nonaka, and Keiichiro HashimotoLight‐sheet microscopy‐based 3D single‐cell tracking reveals a correlation between cell cycle and the start of endoderm cell internalization in early zebrafish developmentDevelopment, Growth and Differentiation, vol.62(7), pp.495--502, https://doi.org/10.1111/dgd.12695, 2020, November, (IF:1.723)
  2. K. Nishimura, J. Hayashida, C. Wang, D.F.E. Ker, and R. BiseWeakly-Supervised Cell Tracking via Backward-and-Forward Propagation16th European Conference on Computer Vision (ECCV2020) 2020, (accepted, Top Conference in Computer Vision, acceptance rate:27%)
  3. H. Tokunaga, B.K. Iwana, Y. Teramoto, A. Yoshizawa, and R. BiseNegative Pseudo Labeling using Class Proportion for Semantic Segmentation in Pathology16th European Conference on Computer Vision (ECCV2020) 2020, (accepted, Top Conference in Computer Vision, acceptance rate:27%)
  4. M. Shimano, Y. Asano, S. Ishihara, R. Bise, and I. SatoImaging Scattering Characteristics of Tissue in Transmitted MicroscopyInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2020), 2020, (accepted, top conference in medial image analysis)
  5. K. Nishimura and R. BiseSpatial-Temporal Mitosis Detection in Phase-Contrast Microscopy Via Likelihood Map Estimation by 3DCNNProceedings of 42st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2020. [pdf]
  6. J. Hayashida, K. Nishimura and R. BiseMPM: Joint Representation of Motion and Position Map for Cell TrackingIEEE CVPR, 2020. (oral, Top Conference in Computer Vision, acceptance rate:22%) [pdf]
201910 papers
  1. R. Bise, K. Abe, H. Hayashi, K. Tanaka, and S. UchidaEfficient Soft-Constrained Clustering for Group-Based LabelingInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2019), 2019, (top conference in medial image analysis, acceptance rate:31%)
  2. K. Nishimura, E.D. Ker, and R. BiseWeakly Supervised Cell Segmentation in Dense by Propagating from Detection MapInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2019), 2019, (top conference in medial image analysis, early acceptance rate:16%)
  3. J. Hayashida, and R. BiseCell Tracking with Deep Learning for Cell Detection and Motion Estimation in Low-Frame-RateInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2019), 2019,(top conference in medial image analysis, early acceptance rate:16%)
  4. S. Harada, H. Hayashi, R. Bise, K. Tanaka, Q. Meng, and S. UchidaEndoscopic Image Clustering with Temporal Ordering Information Based on Dynamic ProgrammingProceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019.
  5. D. Harada, R. Bise, H. Tokunaga, W. Ohyama, S. Oka, T. Fujimori, and S. UchidaScribbles for Metric LearningProceedings of 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019.
  6. H. Tokunaga, Y. Teramoto, A. Yoshizawa, R. BiseAdaptive Weighting Multi-Field-of-View CNN for Semantic Segmentation in PathologyIEEE CVPR, 2019. (Top Conference in Computer Vision, Poster, acceptance rate:25%) pdf
  7. R. Kikkawa, H. Sekiguchi, I. Tsuge, S. Saito and R. BiseSEMI-SUPERVISED LEARNING WITH STRUCTURED KNOWLEDGE FOR BODY HAIR DETECTION IN PHOTOACOUSTIC IMAGEIEEE International Symposium on Biomedical Imaging (ISBI), 2019. (Oral)
  8. H Okawa, M Shimano, Y Asano, R Bise, K Nishino, I SatoEstimation of Wetness and Color From A Single Multispectral ImageIEEE transactions on pattern analysis and machine intelligence, 10.1109/TPAMI.2019.2903496, 2019. (IF:9.455)
  9. S. Saito, R. Bise, et. al.Digital artery deformation on movement of the proximal interphalangeal jointJournal of Hand Surgery(European Volume), doi:1753193418807833, 2019. (IF:2.648)
  10. E. Ker, S. Eom, S. Sanami, R. Bise, et. al.Phase Contrast Time-Lapse Microscopy Datasets with Automated and Manual Cell Tracking AnnotationsScientific Data, doi: 10.1038/sdata.2018.237, 2019. (IF:5.305)
20181 papers
  1. K. Kajiya, R. Bise, et. al.Light-sheet microscopy reveals site-specific 3-dimensional patterns of the cutaneous vasculature and pronounced rarefication in aged skinJournal of Dermatological Science, 92(1), pp.3-5, 2018. (IF: 3.675)
20174 papers
  1. Q. Chen, R. Bise, L. Gu, Y. Zheng, I. Sato, J.N. Hwang, N. Imanishi, and S. AisoVirtual Blood Vessels in Complex Background using Stereo X-ray ImagesICCV Workshop, BioImage Computing, 2017
  2. L. Gu, Y. Zheng, R. Bise, I. Sato, N. Imanishi, and S. AisoSemi-Supervised Learning for Biomedical Image Segmentation via Forest Oriented Super Pixels(Voxels)International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.702-710, 2017,(top conference in medial image analysis, acceptance rate:33%)
  3. M. Shimano, R. Bise, Y. Zheng, and I. SatoSeparation of Transmitted Light and Scattering Components in Transmitted MicroscopyInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2016), pp.702-710, 2017,(top conference in medial image analysis, acceptance rate:33%)
  4. M. Shimano, H. Okawa, Y. Asano, R. Bise, K. Nishino, and I. Sato,Wetness and Color from a Single Multispectral ImageIEEE Conference on Computer Vision and Pattern Recognition(CVPR), pp.3967-3975, 2017，(top conference in computer vision，oral, acceptance rate: 2.5%)．
20163 papers
  1. R. Bise, Y. Zheng, I. Sato, and M. ToiVascular registration in Photoacoustic imaging by low-rank alignment via forground, background, and complement decompositionInternational Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI2016), pp.362-334,2016, (top conference in medial image analysis, early acceptance < 11%)
  2. R. Bise, I. Sato, K. Kajiya, and T. Yamashita3D structure modeling of dense capillaries by multi-objects trackingProceedings of IEEE CVPR2016 Workshop: Computer Vision for Microscopy Analysis(CVMIA) pp.265-270, July. 2016
  3. N. Yasuda, H. Sekine, R. Bise, T. Okano, and T. ShimizuTracing behavior of endothelial cells promotes vascular network formationMicrovascular Research 105, pp.125-131, 2016. (Impact Factor(IF):2.300)
20151 papers
  1. R. Bise and Y. SatoCell Detection Method from Redundant Candidates under the Non-Overlapping ConstraintsIEEE Trans. on Medical Imaging, 34(7), pp.1417-1427, 2015. (IF:3.799)
20132 papers
  1. R. Bise, Y. Maeda, M.H. Kim, and M. Kino-okaCell Tracking Under High Confluency Conditions by Candidate Cell Region Detection Based Association ApproachProceedings of BioMed 2013(oral)
  2. R Bise, N Takahashi, T NishiAn improvement of the design method of cellular neural networks based on generalized eigenvalue minimizationIEEE Trans. Circuits and Systems I: Fundamental Theory and Applications, 50(12), 1569-1574, 2013 (IF:2.303)
20122 papers
  1. AJS. Ribeiro, S. Tottey, RWE. Taylor, R. Bise, T. Kanade, SF. Badylak, and KN. Dahl,Mechanical characterization of adult stem cells from bone marrow and perivascular nichesJournal of biomechanics, 45(7), pp.1280-1287, 2012. (IF:2.496)
  2. S. Eom, S. Huh, D. F. E. Ker, R. Bise, and T. KanadeBTracking of hematopoietic stem cells in microscopy images for lineage determinationIEEE Trans. Biomedical engineering, (accepted,IF:2.233)
20116 papers
  1. S. Huh, E. Ker, R. Bise, M. Chen, and T. KanadeAutomated Mitosis Detection of Stem Cell Populations in Phase-Contrast Microscopy ImagesIEEE Trans. Med. Imaging, 30(3),pp.586-596, 2011 (IF:3.799)
  2. D.F.E. Ker, L.E Weiss, S.N Junkers, M. Chen, Z. Yin, M.F. Sandbothe, S. Huh, S. Eom, R. Bise, E. Osuna-Highley, T. Kanade, and P.G CampbellAn engineered approach to stem cell culture: automating the decision process for real-time adaptive subculture of stem cellsPloS one 6 (11), e27672. (IF:3.534)
  3. R. Bise, T. Kanade, Z. Yin, and S. HuhAutomatic Cell Tracking Applied to Analysis of Cell Migration in Wound Healing Assay Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.6174-6179, 2011(oral)
  4. R. Bise, Z. Yin, and T. KanadeReliable Cell Tracking by Global Data Association Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI)pp.1004-1010, 2011.(oral,acceptance rate < 18%)
  5. S. Huh, S. Eom, R. Bise, Z. Yin, and T. KanadeMitosis Detection for Stem Cell Tracking in Phase-Contrast Microscopy Images Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI)pages 2121-2127, 2011
  6. T. Kanade, Z. Yin, R. Bise, S. Huh, S. Eom, M. Sandbothe and M. ChenCell Image Analysis: Algorithms, System and Applications Proceedings of IEEE Winter Conference on Applications of Computer Vision (WACV)pp.374-381, 2011
20102 papers
  1. S. Eom, R. Bise, and T. KanadeDetection of Hematopoietic Stem Cells in Microscopy Images Using a Bank of Ring Filters Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI)pp.137-140, 2010
  2. Z. Yin, R. Bise, M. Chen, and T. KanadeCell Segmentation in Microscopy Imagery Using a Bag of Local Bayesian Classifiers Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI)pp.125-128, 2010
20091 papers
  1. R. Bise, K. Li, S. Eom, and T. KanadeReliably Tracking Partially Overlapping Neural Stem Cells in DIC Microscopy Image Sequences Proceedings of MICCAI Workshop on OPTMHisEpp.67-77, 2009
20021 papers
  1. R. Bise, N. Takahashi, and T. NishiOn the design method of cellular neural networks for associative memories based on generalized eigenvalue problem Proceedings of IEEE Cellular Neural Networks and Their Applicationspp.515-522, 2002
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Ryoma Bise · Publications

International presentations (not peer reviewed)

21 items
20201 papers
  1. Dan Wang, Xu Zhang, Kazuya Nishimura, Rocky Tuan, Ryoma Bise, Dai Fei Elmer Ker
    Label-Free Cell Detection in Phase Contrast Images Using Artificial Neural Networks
    Orthopaedic Research Society (ORS) Annual Meeting, 2020.3.
20197 papers
  1. Junya Hayashida, Ryoma Bise
    Cell Tracking by estimating cell motions for high-throughput screening
    In Resonance Bio International Symposium, Japan, November 2019.
  2. Nishimura Kazuya, Dai Fei Elmer Ker, Ryoma Bise
    Deep learning for cell segmentation with less annotation
    In Resonance Bio International Symposium, Japan, November 2019.
  3. Kentaro Abe, Hideaki Hayashi, Ryoma Bise, Takuji Kawamura, Naokuni Sakiyama, Kiyohito Tanaka, Seiichi Uchida
    Clustering of Colonoscopic Image with Multi-Task Learning
    The 15th Joint Workshop on Machine Perception and Robotics (MPR2019), Shiga, Japan, 2019.11.
  4. Ryo Kikkawa, Ryoma Bise
    Weakly Supervised Body Hair Detection in Photoacoustic Image
    The 15th Joint Workshop on Machine Perception and Robotics (MPR2019), Shiga, Japan, 2019.11.
  5. Nishimura Kazuya, Dai Fei Elmer Ker, Ryoma Bise
    Weakly supervised Cell Segmentation
    The 15th Joint Workshop on Machine Perception and Robotics (MPR2019), Shiga, Japan, 2019.11.
  6. Junya Hayashida, Ryoma Bise
    Cell Tracking with CNN for Cell Detection and Association
    The 15th Joint Workshop on Machine Perception and Robotics (MPR2019), Shiga, Japan, 2019.11.
  7. Yuki Teramoto, Akihiko Yoshizawa, Ryoma Bise, Hiroki Tokunaga, Naoki Nakajima, and Hironori Haga
    Deep learning for cell segmentation with less annotation
    United States & Canadian Academy of Pathology Annual Meeting (USCAP 2019), March 2019 (Poster presentation，査読有り)
20181 papers
  1. Matsumoto Y, Gu L, Bise R, Asao Y, Sekiguchi H, Yoshikawa A, Ishii T, Takada M, Kataoka M, Sakurai T, Yagi T, Sato I, Togashi K, Shiina T, and Toi M.
    Machine learning-based structural analysis and oxygen saturation measurement of tumor-associated vessels in breast cancer using a photoacoustic tomography system
    USA, San Antonio Breast Cancer Symposium 2018.
20162 papers
  1. K.Kajiya, R.Bise, C.Seidel, I. Sato, T. Yamashita, and M. Detmar
    Cleaning of a human skin and its application for the three-dimensional visualization of the vasculature
    Journal of Investigative Dermatology, 136, 9, S254, 2016.
  2. A. Kondow, K. Ohnuma, S. Nonaka, Y. Kamei, R. Bise, Y. Sato, T. Kobayashi, and K. Hashimoto
    In vivo measurement of the Nodal signal followed by 3D tracking during early zebrafish development
    JSDB Special Symposium: Frontier of Developmental Biology, June 2016.
20141 papers
  1. R. Bise et al.
    3D Cell Tracking Under Dense Cell Culture Conditions by Preserving the Structure of Neighbor Cells
    IEEE International Symposium on Biomedical Imaging(ISBI) 2014
20113 papers
  1. R. Bise and T. Akai
    Cell Migration Assay Kit based on Smart Surface and Cell Tracking
    First Workshop on Computer Vision Tracking of Cell Populations 2011 (oral)
  2. R. Bise, Z. Yin, S. Huh, S. Eom, and T. Kanade
    Global Tree Structure Association Method for Tracking Cells and Creating Lineage Tree
    First Workshop on Computer Vision Tracking of Cell Populations 2011 (Poster)
  3. E.D.F. Ker, L. Weiss, S. Junkers, M. Chen, Z. Yin, E. Highley, S. Huh, M.F. Sandbothe, S. Eom, R. Bise, T. Kanade, and P. Campbell
    Towards Robotic Subculture of Cells: Automating the Decision Process for Real Time Adaptive Subculture of Stem Cells
    First Workshop on Computer Vision Tracking of Cell Populations 2011 (Poster)
20104 papers
  1. R. Bise et al.
    Cell Image Analysis Technology Applications
    Bioimage Informatics 2010
  2. R. Bise et al.
    Real-time System for Microscope Imaging, Cell Tracking, and Adaptive Culturing
    Bioimage Informatics 2010 (Poster)
  3. T. Kanade, M.F. Sandbothe, D.F.E. Ker, S. Eom, R. Bise, S.Huh, Z. Yin, and M. Chen
    Real-time System for Microscope Imaging, Cell Tracking, and Adaptive Culturing
    Bioimage informatics, 2010, (Poster)
  4. T. Kanade, S. Eom, R. Bise, Seung-il Huh, Zhaozheng Yin, and Mei Chen
    Cell Image Analysis Technology Applications
    Bioimage informatics, 2010, (Poster)
20092 papers
  1. R. Bise, Kang Li, and Takeo Kanade
    Cell Tracking with Occlusion Handling
    ntel Labs Pittsburgh Open House 2009
  2. R. Bise, K. Li, and T. Kanade
    Automated Stem Cell Tracking through Long-Term Partial Overlap
    Annual meeting of Biomedical Engineering Society(BMES), 2009
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Ryoma Bise · Publications

Domestic conferences and symposia

74 items

Titles are shown as presented. Links for MIRU 2025–2026 point to author publication records.

202612 items
  1. 菅田菜央・志久開人・松尾信之介・備瀬竜馬Gleason Scoreを活用した弱教師あり学習による病理画像のセグメンテーションPRMU 2026 · PRMU2025-35
  2. 見田壮一・竹崎隼平・備瀬竜馬拡散モデルによる3次元CT画像からの血管中心線推定PRMU 2026 · PRMU2025-36
  3. 井上颯人・竹崎隼平・原田翔太・備瀬竜馬Schrödinger Bridgeによる細胞セグメンテーションPRMU 2026 · PRMU2025-37
  4. 松尾信之介・末廣大貴・備瀬竜馬ラベル比率からの学習のための比率エントロピーに基づくカリキュラム学習MIRU 2026
  5. Phuong Ngoc Nguyen・志久開人・備瀬竜馬・内田誠一・松尾信之介能動学習における弱教師としての視覚言語モデルの活用MIRU 2026 · IS3-069
  6. 筬島未輝夫・松尾信之介・志久開人・備瀬竜馬距離加重サンプリングに基づく医療検査項目の逐次選択MIRU 2026
  7. 菅田菜央・志久開人・松尾信之介・備瀬竜馬PrimaryおよびSecondaryラベルを用いた弱教師あり病理画像セグメンテーションMIRU 2026
  8. 劉易庭・瀬尾惟周・志久開人・備瀬竜馬Optimal Transport based Anatomical Labeling of Coronary CenterlinesJoint Conference of Electrical, Electronics and Information Engineers in Kyushu 2026 · 07-1P-01
  9. 本田凌大・志久開人・原田翔太・備瀬竜馬ハイパーネットワークを用いたドメイン適応Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2026 · 09-2A-02
  10. 中村嘉樹・松尾信之介・志久開人・原田翔太・備瀬竜馬視覚基盤モデルと視覚言語モデルの共同によるゼロショット画像分類の性能改善Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2026 · 09-2A-03
  11. 木下結雅・竹崎隼平・備瀬竜馬複数タスク学習を用いた拡散モデルによる網膜血管セグメンテーションJoint Conference of Electrical, Electronics and Information Engineers in Kyushu 2026 · 09-2P-03
  12. 内田陸斗・原田翔太・久保田優吾・志久開人・備瀬竜馬双曲埋め込みを用いた順序分類のための表現学習Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2026 · 09-2P-05
202517 items
  1. 西村和也・備瀬竜馬・小嶋泰弘空間トランスクリプトームを用いた病理画像認識PRMU 2025 · PRMU2024-34
  2. 江口達大・竹崎隼平・備瀬竜馬拡散モデルによる画像とクラスの共起学習PRMU 2025 · PRMU2024-42
  3. 西村和也・廣瀬遥香・備瀬竜馬・志久開人・小嶋泰弘空間トランスクリプトミクスにおけるバッチ効果と確率的ノイズ下での相対発現学習MIRU 2025
  4. 志久開人・西村和也・松尾信之介・小嶋泰弘・備瀬竜馬Spatial Transcriptomics Estimation via Informative Gene SelectionMIRU 2025
  5. 瀬尾惟周・志久開人・仲野泰啓・的場哲哉・備瀬竜馬冠動脈CT画像を用いた石灰化切削必要性推定のためのMultimodal ModelMIRU 2025
  6. Takamasa Yamaguchi, Brian Kenji Iwana, Ryoma Bise, Shota Harada, Takumi Okuo, Kiyohito Tanaka, Kaito ShikuLeveraging Patient-Level Diagnosis for Domain Adaptation in Ulcerative Colitis Severity AssessmentMIRU 2025
  7. 竹崎隼平・備瀬竜馬・松尾信之介拡散モデルにおける推定ノイズのCutMixによるデータ拡張MIRU 2025
  8. 西村和也・備瀬竜馬・松尾信之介・廣瀬遥香・小嶋泰弘病理スライド画像からの遺伝子発現推定のための細胞種毎のプロトタイプを組み込んだNeural NetworkMIRU 2025
  9. 田原聖士・竹崎隼平・備瀬竜馬拡散モデルを用いた細胞検出MIRU 2025
  10. 石丸大晟・竹崎隼平・豊田祥史・備瀬竜馬条件付き拡散モデルの不確実性分析MIRU 2025
  11. 江口達大・竹崎隼平・備瀬竜馬拡散モデルによる画像とクラスの共起生成MIRU 2025
  12. 菅田菜央・志久開人・松尾信之介・備瀬竜馬比率に基づく弱教師あり学習による病理組織認識Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2025 · 04-2A-01
  13. 筬島未輝夫・松尾信之介・志久開人・備瀬竜馬医療検査項目の適応的選択法Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2025 · 04-2A-07
  14. 見田壮一・竹崎隼平・備瀬竜馬生成モデルによるCT画像からの血管抽出Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2025 · 04-2P-03
  15. 呉哲・江口達大・竹崎隼平・備瀬竜馬拡散モデルに基づく画像とクラスラベルの同時生成Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2025 · 04-2P-04
  16. 相澤卓輝・竹崎隼平・備瀬竜馬・原田翔太順序クラス分類におけるデータ拡張Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2025 · 04-2P-07
  17. 仲野泰啓・日野立誠・瀬尾惟周・備瀬竜馬・的場哲哉冠動脈インターベンションにおける心臓CT画像を用いたAIの活用Japanese Association of Cardiovascular Intervention and Therapeutics 2025 · 2025年7月
202414 items
  1. 志久開人・西村和也・末廣大貴・備瀬竜馬最重症度ラベルを用いたマルチインスタンス学習PRMU 2024 · PRMU2023-61
  2. 久保俊介・松尾信之介・末廣大貴・寺田和弘・伊藤寛朗・吉澤明彦・備瀬竜馬巨大バッグに対するLearning from Label Proportionのための理論的ラベル比率摂動MIRU 2024 · OS-1A-02
  3. Shota Harada, Ryoma Bise, Kiyohito Tanaka, Seiichi UchidaSemi-supervised Domain Adaptation Using Class Order for Severity ClassificationMIRU 2024 · OS-1B-06
  4. 門田健明・備瀬竜馬・田中聖人・早志英朗重症度比較における不確実性と信頼度に基づく教師なしドメイン適応MIRU 2024 · OS-1D-03
  5. 志久開人・西村和也・末廣大貴・田中聖人・備瀬竜馬潰瘍性大腸炎の重症度推定を目的とした選択的集約トランスフォーマーによる順序ありクラスのマルチインスタンス学習MIRU 2024 · IS-1-053
  6. 江口達大・竹崎隼平・備瀬竜馬撮像条件情報を用いたガイダンスを導入した拡散モデルによる光超音波画像の画質改善MIRU 2024 · OS-2D-04
  7. 田原聖士・備瀬竜馬異なるスケール間での画像位置合わせ手法の検討MIRU 2024 · IS-2-178
  8. 松尾信之介・末廣大貴・計良宥志・内田誠一・備瀬竜馬Prototypeに基づいたAttentionによるLearning from Label ProportionsMIRU 2024 · OS-3A-06
  9. 志久開人・松尾信之介・末廣大貴・備瀬竜馬多数派クラスラベルからの学習を目的とした数え上げネットワークMIRU 2024 · IS-3-046
  10. 秋庭孔樹・西村和也・備瀬竜馬テキスト情報を用いた病理画像分類Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2024 · 10-2A-03
  11. 石丸大晟・竹崎隼平・備瀬竜馬拡散モデルを利用した分布外検出Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2024 · 10-2P-06
  12. Takamasa Yamaguchi, Kaito Shiku, Ryoma Bise, Brian Kenji IwanaMulti Instance Learningとドメイン適応を用いた医療画像診断Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2024 · 10-2P-10
  13. 瀬尾惟周・志久開人・仲野泰啓・的場哲哉・備瀬竜馬冠動脈CTを用いた冠動脈疾患の手術支援Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2024 · 10-2P-11
  14. Kazuhiro Terada, Akihiko Yoshizawa, Xiaoqing Liu, Ryoma Bise, Masanobu Kitagawa, Masashi Fukayama, Hironori HagaDevelopment of a deep learning model to discriminate uterine cervical intraepithelial neoplasia biopsies using whole slide imagingsJapanese Society of Pathology 2024 · 2024年3月
202318 items
  1. 西村和也・刀谷在美・中馬新一郎・備瀬竜馬部分的なアノテーションを用いた細胞分裂検出PRMU 2023 · PRMU2022-67
  2. 松尾信之介・末廣大貴・内田誠一・備瀬竜馬部分的なラベル比率からの学習PRMU 2023 · PRMU2022-92
  3. 重安勇輝・原田翔太・倉田麻理代・寺田和弘・中島直樹・吉澤明彦・阿部浩幸・牛久哲男・備瀬竜馬年代情報を用いた病理画像のためのドメイン一般化PRMU 2023 · PRMU2023-16
  4. 山根健寛・津下到・齊藤晋・備瀬竜馬医用画像セグメンテーションにおけるPU Learningを用いた疑似ラベル選択PRMU 2023 · PRMU2023-17
  5. 松尾信之介・末廣大貴・内田誠一・伊藤寛朗・寺田和弘・吉澤明彦・備瀬竜馬WSIに対する部分的なラベル比率からの学習MIRU 2023 · OS1B-L2
  6. 奥尾拓己・西村和也・伊藤寛朗・寺田和弘・吉澤明彦・備瀬竜馬Learning from Label Proportionによる陽性腫瘍の比率推定MIRU 2023 · OS2B-S6
  7. 西村和也・刀谷在美・中馬新一郎・備瀬竜馬時間順序反転を用いたデータセット作成による部分的なアノテーションによる細胞分裂検出MIRU 2023 · OS2B-S7
  8. 浅海標徳・松尾信之介・末廣大貴・備瀬竜馬クラス比率学習におけるバッグ単位のデータ拡張MIRU 2023 · OS6B-L1
  9. 原田翔太・備瀬竜馬・田中聖人・内田誠一クラスの順序関係を利用した半教師付きドメイン適応MIRU 2023 · IS3-36
  10. 山根健寛・津下到・齊藤晋・備瀬竜馬医用画像セグメンテーションにおけるPU Learningを用いた疑似ラベル選択MIRU 2023 · IS3-94
  11. 重安勇輝・原田翔太・荒木健吾・吉澤明彦・寺田和弘・備瀬竜馬病理画像セグメンテーションにおける腫瘍の長径を用いた弱教師付き半教師学習の提案MIRU 2023 · IS3-95
  12. 江口達大・備瀬竜馬深層学習を用いた光超音波画像の画質改善Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2023 · 05-2A-04
  13. 田原聖士・備瀬竜馬異なるスケールの画像間の位置合わせ手法の検討Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2023 · 05-2A-08
  14. 井上颯人・西村和也・備瀬竜馬対照学習を用いた細胞形状に頑健な細胞検出Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2023 · 05-2P-07
  15. 久保俊介・松尾信之介・備瀬竜馬信頼区間を考慮したLLP手法による巨大バッグからの学習Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2023 · 05-2P-08
  16. Motokazu Uchigashima, Risa Iguchi, Kazuma Fujii, Pratik Kumar, Manabu Abe, Motohiro Nozumi, Michihiro Igarashi, Kenji Sakimura, Ryoma Bise, Luke D. Lavis, Takayasu MikuniDevelopment of Single-Cell, Spatiotemporal, Quantitative Imaging Method for Endogenous Proteins in Mammalian BrainsJapanese Association of Anatomists 2023 · 第128回
  17. 重安勇輝・原田翔太・倉田麻理代・寺田和弘・中島直樹・吉澤明彦・阿部浩幸・牛久哲男・備瀬竜馬WSI特徴を用いたドメイン一般化IEICE Medical Imaging (MI) 2023 · 2023年3月
  18. 山根健寛・原田翔太・津下到・齊藤晋・備瀬竜馬光超音波画像における反射ノイズの推定IEICE Medical Imaging (MI) 2023 · 2023年3月
202213 items
  1. 重安勇輝・原田翔太・荒木健吾・吉澤明彦・寺田和弘・寺本祐記・備瀬竜馬病理画像における腫瘍領域の空間分布に基づく半教師学習PRMU 2022 · PRMU2022-8
  2. 浅海標徳・西村和也・備瀬竜馬特徴量ワーピングを導入した時間情報集約によるマルチオブジェクトトラッキングPRMU 2022 · PRMU2022-33
  3. 藤井和磨・末廣大貴・備瀬竜馬部分的な教師データを用いた細胞検出PRMU 2022 · PRMU2022-55
  4. 松尾信之介・備瀬竜馬・内田誠一・末廣大貴オンライン予測理論に基づく擬似ラベル手法によるクラス比率からの学習PRMU 2022 · PRMU2022-57
  5. 重安勇輝・原田翔太・荒木健吾・吉澤明彦・寺田和弘・寺本祐記・備瀬竜馬病理画像セグメンテーションにおける腫瘍領域の空間分布に基づく疑似ラベル選択法の提案MIRU 2022 · OL3B-1
  6. 浅海標徳・林田純也・西村和也・備瀬竜馬Self-Attentionによる大局的時間情報を考慮した複数物体トラッキングMIRU 2022 · OS2A-6
  7. Takanori Asanomi, Kazuya Nishimura, Heon Song, Junya Hayashida, Hiroyuki Sekiguchi, Takayuki Yagi, Imari Sato, Ryoma BiseDeep Non-Rigid Registration for Noisy-and-Corrupted ImagesMIRU 2022 · OS3A-7
  8. 西村和也・備瀬竜馬複数種の弱教師を用いたsingle instance pastingによる細胞画像セグメンテーションMIRU 2022 · OS3A-8
  9. Shota Harada, Ryoma Bise, Kengo Araki, Akihiko Yoshizawa, Kazuhiro Terada, Mariyo Kurata-Rokutan, Naoki Nakajima, Hiroyuki Abe, Tetsuo Ushiku, Seiichi UchidaSemi-Supervised Domain Adaptation for Class-Imbalanced DatasetMIRU 2022 · OS3A-9
  10. 志久開人・白井洸充・石原健・備瀬竜馬線虫の時系列3D神経細胞データに対する神経細胞追跡Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2022 · 04-1A-01
  11. 奥尾拓己・西村和也・伊藤寛朗・吉澤明彦・備瀬竜馬病理画像における腫瘍及び正常細胞検出Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2022 · 04-1A-02
  12. 劉暁慶・荒木健吾・寺田和弘・吉澤明彦・備瀬竜馬病理画像セグメンテーションのための半教師ドメイン適応Joint Conference of Electrical, Electronics and Information Engineers in Kyushu 2022 · 04-1A-03
  13. 内田誠一・備瀬竜馬バイオメディカル画像解析に関する Label Efficient LearningBioimaging Society of Japan 2022 · 第31回・シンポジウム1
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Ryoma Bise · Publications

Invited talks, seminars and tutorials

11 items
20254 items
  1. Ryoma Bise医工連携における画像情報学研究 ～不完全な教師データを用いた機械学習～Symposium on Sensing via Image Information SSII2025, 2025 · Tutorial
  2. Ryoma Biseラベル効率的な学習によるバイオ医療画像認識Cancer Research Institute Seminar（2025年4月）, 2025 · Seminar
  3. Ryoma Biseバイオ医療画像のための少数データに対する機械学習手法Quantitative Biology Meeting, Kyushu Caravan2025（1月11日）, 2025 · Tutorial
  4. Ryoma Bise計測インフォマティクス・データ解析81st Annual Meeting of the Japanese Society of Microscopy・IMB-7, 2025 · Invited talk
20243 items
  1. Ryoma Biseバイオ医療画像認識におけるLabel Efficient LearningVision Engineering Workshop ViEW2024, 2024 · Keynote talk
  2. Ryoma Bise教師データの不足を補う：少数データに対する機械学習手法概要43rd Annual Meeting of the Japanese Society of Medical Imaging Technology JAMIT2024, 2024 · Tutorial
  3. Ryoma Bise基礎生物学研究所セミナー（12月27日）National Institute for Basic Biology, 2024 · Seminar
20233 items
  1. Ryoma Bise第69回日本病理学会秋期特別総会Japanese Society of Pathology, 2023 · Invited talk
  2. Ryoma Bise建設土木AIシンポジウムCivil Engineering AI Symposium, 2023 · Talk
  3. Ryoma Bise新潟大学セミナーNiigata University, 2023 · Seminar
20221 items
  1. Seiichi Uchida and Ryoma Biseバイオメディカル画像解析に関する Label Efficient Learning31st Annual Meeting of the Bioimaging Society of Japan, Symposium 1, 2022 · Symposium talk
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Ryoma Bise · Publications

PhD thesis

1 items
  1. Ryoma Bise
    Cell Tracking Under Dense Cell Culture Conditions for Cell Behavior Analysis
    Graduate School of Interdisciplinary Information Studies, The University of Tokyo, 2015.05
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