D. Nishiyama, H. Miyoshi, N. Hashimoto, K. Ohshima, H. Hontani, I. Takeuchi, and J. Sakuma, “Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion,” Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lecture Notes in Computer Science, 15971: 320-330, 2025.
R. Koga, T. Yokota, H. Miyoshi, N. Hashimoto, I. Takeuchi, and H. Hontani, Explainable Grading for Follicular Lymphoma via Quantitative Criteria Based on Composition Ratio of Cell Types, Medical Imaging Technology, 43(4): 110-115, 2025. (In Japanese)
N. Hashimoto, Explainable Grading for Follicular Lymphoma via Quantitative Criteria Based on Composition Ratio of Cell Types, Medical Imaging Technology, 43(4): 116-121, 2025. (In Japanese)
S. Takeno, Y. Okura, Y. Inatsu, T. Aoyama, T. Tanaka, S. Akahane, H. Hanada, N. Hashimoto, T. Murayama, H. Lee, S. Kojima, and I. Takeuchi, “Distributionally Robust Active Learning for Gaussian Process Regression,” Proceedings of the 42nd International Conference on Machine Learning, 267: 58339-58358, 2025.
R. Koga, S. Koide, H. Tanaka, K. Taguchi, M. Kugler, T. Yokota, K. Ohshima, H. Miyoshi, M. Nagaishi, N. Hashimoto, I. Takeuchi, and H. Hontani, “A study of criteria for grading follicular lymphoma using a cell type classifier from pathology images based on complementary-label learning,” Micron, 184: 103663, 2024.
N. Hashimoto, H. Hanada, H. Miyoshi, M. Nagaishi, K. Sato, H. Hontani, K. Ohshima, and I. Takeuchi, “Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma,” Journal of Pathology Informatics, 15: 100359, 2024.
M. Nagaishi, H. Miyoshi, M. Kugler, K. Sato, K. Kohno, M. Takeuchi, K. Yamada, T. Furuta, N. Hashimoto, I. Takeuchi, H. Hontani, and K. Ohshima, “The Detection of Neoplastic Cells Using Objective Cytomorphologic Parameters in Malignant Lymphoma,” Laboratory Investigation, 104(3): 100302, 2024.
J. Butke, N. Hashimoto, I. Takeuchi, H. Miyoshi, K. Ohshima, and J. Sakuma, “Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping,” Lecture Notes in Computer Science, 14349: 114-123, 2024.
Y. Fuse, K. Takeuchi, N. Hashimoto, Y. Nagata, Y. Takagi, T. Nagatani, I. Takeuchi, and R. Saito, “Deep learning based identification of pituitary adenoma on surgical endoscopic images: a pilot study,” Neurosurgical Review, 46(1): 291, 2023.
H. Hanada, N. Hashimoto, K. Taji, and I. Takeuchi, “Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications,” Neural Computation, 35(12): 1970-2005, 2023.
N. Hashimoto, Y. Takagi, H. Masuda, H. Miyoshi, K. Kohno, M. Nagaishi, K. Sato, M. Takeuchi, T. Furuta, K. Kawamoto, K. Yamada, M. Moritsubo, K. Inoue, Y. Shimasaki, Y. Ogura, T. Imamoto, T. Mishina, K. Tanaka, Y. Kawaguchi, S. Nakamura, K. Ohshima, H. Hontani, and I. Takeuchi, “Case-based similar image retrieval for weakly annotated large histopathological images of malignant lymphoma using deep metric learning,” Medical Image Analysis, 85: 102752, 2023.
Y. Takagi, N. Hashimoto, H. Masuda, H. Miyoshi, K. Ohshima, H. Hontani, and I. Takeuchi, “Transformer-based personalized attention mechanism for medical images with clinical records,” Journal of Pathology Informatics, 14: 100185, 2023.
N. Hashimoto, K. Ko, T. Yokota, K. Kohno, M. Nakaguro, S. Nakamura, I. Takeuchi, and H. Hontani, “Subtype classification of malignant lymphoma using immunohistochemical staining pattern,” International Journal of Computer Assisted Radiology and Surgery, 17(7): 1379-1389, 2022.
K. Tanizaki, N. Hashimoto, Y. Inatsu, H. Hontani, and I. Takeuchi, “Computing Valid P-Values for Image Segmentation by Selective Inference,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9553-9562, 2020.
N. Hashimoto, D. Fukushima, R. Koga, Y. Takagi, K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani, and I. Takeuchi, “Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3852-3861, 2020.
Y. Hirano, T. Ito, N. Hashimoto, S. Kido and K. Suzuki, "Massive-training artificial neural network deep learning in computer-aided diagnosis for chest and abdomen," Medical Imaging Technology, Vol. 35, No. 4, pp. 194-199, Sept. 2017. (In Japanese)
S. Mabu, M. Obayashi, T. Kuremoto, N. Hashimoto, Y. Hirano and S. Kido, "Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns," International Journal of Computer Assisted Radiology and Surgery, Vol. 12, No. 3, pp. 519-528, Aug. 2017.
T. Ohnishi, Y. Nakamura, T. Tanaka, T. Tanaka, N. Hashimoto, H. Haneishi, T. T. Batchelor, E. R. Gerstner, J. W. Taylor, M. Snuderl, and Y. Yagi, “Deformable image registration between pathological images and MR image via an optical macro image,” Pathology - Research and Practice, 212(10): 927-936, 2016.
N. Hashimoto, P.A. Bautista, H. Haneishi, M. Snuderl and Y. Yagi, "Development of a 2D image reconstruction and viewing system for histological images from multiple tissue blocks: towards high-resolution whole-organ 3D histological images," Pathobiology, Vol. 83, No. 2-3, pp. 127-139, Apr. 2016.
N. Hashimoto, "Practical application of digital images in pathology: toward standardization," IEICE Fundamental Review, Vol. 9, No. 3, pp. 214-218, Jan. 2016. (In Japanese)
P.A. Bautista, N. Hashimoto and Y. Yagi, "Color standardization for whole slide imaging using a color calibration slide," Journal of Pathology Informatics, Vol. 5, No. 1, 4, Jan. 2014.
N. Hashimoto, Y. Murakami, M. Yamaguchi and N. Ohyama, "A digital H&E staining method for multispectral images of H-only stained tissue specimens and its application," IEICE Transactions on Information and Systems, Vol. J96-D, No. 4, pp. 844-853, Apr. 2013. (In Japanese)
N. Hashimoto, P.A. Bautista, M. Yamaguchi, N. Ohyama and Y. Yagi, "Referenceless image quality evaluation for whole slide imaging," Journal of pathology Informatics, Vol. 3, No. 1, 9, Mar. 2012.
N. Hashimoto, Y. Murakami, P.A. Bautista, M. Yamaguchi, T. Obi, N. Ohyama, K. Uto and Y. Kosugi, "Multispectral image enhancement for effective visualization," Optics Express, Vol. 19, No. 10, pp. 9315-9329, Apr. 2011.
N. Hashimoto, S. Imaizumi, M. Fujiyoshi, and H. Kiya, “Hierarchical encryption using short encryption keys for scalable access control of JPEG 2000 coded images,” Proceedings of the IEEE International Conference on Image Processing (ICIP), 3116-3119, 2008.
W. Kanagae, T. Yokota, N. Hashimoto, I. Takeuchi, H. Miyoshi, Y. Noto, R. Koga, and H. Hontani, “Discovering Subtype-specific Cell Nuclei in Malignant Lymphoma via Conformal Prediction,” Asian Conference on Computer Vision (ACCV), 2026.
K. Ohara, K. Taguchi, T. Yokota, N. Hashimoto, I. Takeuchi, H. Miyoshi, R. Koga, and H. Hontani, “Foundation-model-based Hyperbolic Multi-scale Representation Learning and Density-ratio Analysis for Subtype-specific Nuclei Discovery,” Asian Conference on Computer Vision (ACCV), 2026.
R. Koga, T. Yokota, N. Hashimoto, H. Miyoshi, I. Takeuchi, and H. Hontani, “Linearized MIL Attention Reveals a Traversable Direction in the Virchow2 Embedding Space,” MICCAI 2026 Workshop COMPAYL, 2026.
K. Murakami, R. Koga, T. Yokota, I. Takeuchi, N. Hashimoto, H. Miyoshi, and H. Hontani, “Pathological Image Generation Explicitly Incorporating Statistical Properties of Tissues,” Computer Assisted Radiology and Surgery (CARS), 2026.
N. Hashimoto, H. Miyoshi, and I. Takeuchi, “Reliable multi-class classification of malignant lymphoma pathology images via conformal prediction,” Computer Assisted Radiology and Surgery (CARS), 2026, Nagoya, Japan.
D. Nishiyama, H. Miyoshi, N. Hashimoto, K. Ohshima, H. Hontani, I. Takeuchi, and J. Sakuma, “Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion,” International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2025.
S. Takeno, Y. Okura, Y. Inatsu, T. Aoyama, T. Tanaka, S. Akahane, H. Hanada, N. Hashimoto, T. Murayama, H. Lee, S. Kojima, and I. Takeuchi, “Distributionally Robust Active Learning for Gaussian Process Regression,” 42nd International Conference on Machine Learning (ICML), 2025.
R. Koga, T. Yokota, K. Ohshima, H. Miyoshi, N. Hashimoto, I. Takeuchi, and H. Hontani, “Generation of Counterfactual Pathology Images of Malignant Lymphoma using Diffusion Models,” International Forum on Medical Imaging in Asia (IFMIA), 2025.
J. Onoki, Y. Tsuchimoto, T. Yokota, K. Ohshima, H. Miyoshi, N. Hashimoto, I. Takeuchi, and H. Hontani, “Construction of a Pathology Image Classifier with Quantitative Uncertainty Evaluation for Cancer Diagnosis Assistance,” International Forum on Medical Imaging in Asia (IFMIA), 2025.
S. Kawakami, N. Hashimoto, H. Miyoshi, J. Sakuma, H. Hontani, K. Ohshima, and I. Takeuchi, “Machine Learning for Classifying and Generating Videos of Pathological Image Observation Process,” International Forum on Medical Imaging in Asia (IFMIA), 2025.
R. Koga, M. Kugler, T. Yokota, K. Ohshima, H. Miyoshi, M. Nagaishi, N. Hashimoto, I. Takeuchi, and H. Hontani, “Modification of DDIM Encoding for Generating Counterfactual Pathology Images of Malignant Lymphoma,” International Conference on Computer Vision Theory and Applications (VISAPP), 2024.
R. Koga, M. Kugler, T. Yokota, K. Ohshima, H. Miyoshi, M. Nagaishi, N. Hashimoto, I. Takeuchi, and H. Hontani, “Generation of Counterfactual Images to Construct Criteria for Quantitatively Evaluating Subtypes in Malignant Lymphoma,” International Forum on Medical Imaging in Asia (IFMIA), 2023.
S. Koide, M. Kugler, T. Yokota, K. Ohshima, H. Miyoshi, M. Nagaishi, N. Hashimoto, I. Takeuchi, and H. Hontani, “Construction of Classifier for Malignant Lymphoma Nuclei Using Label Propagation,” International Forum on Medical Imaging in Asia (IFMIA), 2023.
H. Tanaka, N. Hashimoto, T. Yokota, M. Kugler, K. Ohshima, H. Miyoshi, M. Nagaishi, I. Takeuchi, and H. Hontani, “Graph Neural Network for Identification of Malignant Lymphoma Subtypes and Class Activation Visualization of Cell Tissue Anomality,” International Forum on Medical Imaging in Asia (IFMIA), 2023.
R. Koga, N. Hashimoto, T. Yokota, M. Nakaguro, K. Kohno, S. Nakamura, I. Takeuchi, and H. Hontani, “Detection of DLBCL Regions in H&E Stained Whole Slide Pathology Images Using SC-CNN,” International Forum on Medical Imaging in Asia (IFMIA), 2021.
R. Koga, N. Hashimoto, T. Yokota, M. Nakaguro, K. Kohno, S. Nakamura, I. Takeuchi, and H. Hontani, “Stain Transfer for Automatic Annotation of Malignant Lymphoma Regions in H&E Stained Whole Slide Histology Images,” International Forum on Medical Imaging in Asia (IFMIA), 2021.
N. Hashimoto, D. Fukushima, R. Koga, Y. Takagi, K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani and I. Takeuchi, "Multi-scale domain-adversarial multiple-instance CNN for cancer subtype classification with unannotated histopathological images," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 3852-3861, June 2020.
K. Tanizaki, N. Hashimoto, Y. Inatsu, H. Hontani and I. Takeuchi, "Computing valid p-values for image segmentation by selective inference," Proc. IEEE Conference on Computer Vision and Pattern Recognition, pp. 9553-9562, June 2020.
S. Kido, K. Murakami, N. Hashimoto, Y. Hirano, S. Mabu, and K. Suzuki, “Deep Learning Techniques for Automated Segmentation of Diffuse Lung Disease Opacities on CT Images,” Radiological Society of North America (RSNA), 2018.
N. Hashimoto, K. Suzuki, J. Liu, Y. Hirano, H. MacMahon and S. Kido, "Deep neural network convolution (NNC) for three-class classification of diffuse lung disease opacities in high-resolution CT (HRCT): consolidation, ground-glass opacity (GGO), and normal opacity," Proc. SPIE Medical Imaging, Vol. 10575, 1057536, Feb. 2018.
S. Kido, Y. Hirano, and N. Hashimoto, “Computer-aided Classification of Pulmonary Diseases: Feature Extraction Based Method versus Non-feature Extraction Based Method,” International Workshop on Advanced Image Technology (IWAIT), 2017.
K. Murakami, S. Kido, N. Hashimoto, Y. Hirano, K. Wakasugi, and K. Inai, “Computer-aided Classification of Diffuse Lung Disease Patterns Using Convolutional Neural Network,” 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS), 2017.
S. Kido, N. Hashimoto, and Y. Hirano, “Computer-Aided Analysis of Diffuse Lung Diseases on Chest Radiographs: Feature Extraction Based Method versus Non-Feature Extraction Based Method (I),” 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017.
T. Odo, S. Kido, Y. Hirano, N. Hashimoto, and N. Tanaka, “Radiomic Analysis of Pulmonary Adenocarcionomas on CT Images,” 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017.
N. Hashimoto, S. Kido, and Y. Hirano, “Classification of diffuse lung diseases using convolutional neural network for practical application,” International Workshop on Advanced Image Technology (IWAIT), 2016, Busan, South Korea.
S. Mabu, M. Obayashi, T. Kuremoto, N. Hashimoto, Y. Hirano, and S. Kido, “Unsupervised Class Labeling of Diffuse Lung Diseases Using Frequent Attribute Patterns,” 30th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS), 2016.
S. Kido, N. Hashimoto, Y. Hirano, H. Kim, H. Kimura, S. Noriki, K. Inai, and H. Shouno, “Multi-disciplinary Computational Anatomy Assisting Radiology,” 30th International Congress and Exhibition on Computer Assisted Radiology and Surgery (CARS), 2016.
N. Hashimoto, P.A. Bautista and Y. Yagi, "Importance of image quality and its standardization in digital pathology," Proc. The 7th International Workshop on Image Media Quality and its Applications, pp. 1-4, Sept. 2014. (Invited)
T. Ohnishi, T. Tanaka, Y. Nakamura, N. Hashimoto, H. Haneishi, J. Taylor, M. Snuderl, and Y. Yagi, “Connection and Deformation of Pathological Images via a Macro Image for Comparing Different Modality Images of Brain Tumor,” 2nd Congress of International Academy of Digital Pathology, 2014.
Y. Nakamura, T. Tanaka, T. Ohnishi, N. Hashimoto, H. Haneishi, J. Taylor, M. Snuderl, and Y. Yagi, “Registration Between Pathological Image and MR Image for Comparing Different Modality Images of Brain Tumor,” 2nd Congress of International Academy of Digital Pathology, 2014.
N. Hashimoto, T. Tanaka, M. Snuderl, and Y. Yagi, “Development of a Viewing System for a Whole Tissue from Multiple Tissue Blocks,” American Society for Investigative Pathology 2013 Annual Meeting, 2013.
N. Hashimoto and Y. Yagi, “Development of an Automated 2D Image Reconstruction Algorithm for Multiple Tissue Blocks: Toward High-resolution Whole Organ 3D Images,” 26th European Congress of Pathology, 2013.
N. Hashimoto, P. A. Bautista, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Image Quality Evaluation Methods for Whole Slide Imaging,” New England Science Symposium, 2011.
N. Hashimoto, P. A. Bautista, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Image Quality Evaluation Methods for Whole Slide Scanning,” 64th Annual Meeting of the MGH Scientific Advisory Committee, 2011.
F. Yang, K. Wakunami, K. Shinoda, N. Hashimoto, and M. Yamaguchi, “Digital Color Management in Holoprinter,” Digital Holography and Three-Dimensional Imaging, 2011.
N. Hashimoto, P. A. Bautista, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Development of Image Quality Evaluation Method for Whole Slide Imaging,” 1st Congress of the International Academy of Digital Pathology, 2011.
N. Hashimoto, Y. Murakami, M. Yamaguchi, N. Ohyama, K. Uto and Y. Kosugi, "Application of multispectral color enhancement for remote sensing," Proc. SPIE Remote Sensing, Vol. 8180, 818006, Sept. 2011.
N. Hashimoto, Y. Murakami, M. Yamaguchi, T. Obi and N. Ohyama, "Color enhancement of multispectral images for effective visualization," Proc. IS&T 5th European Conference on Colour in Graphics, Imaging and Vision, pp. 282–288, June 2010.
N. Hashimoto, S. Imaizumi, M. Fujiyoshi and H. Kiya, "Hierarchical encryption using short encryption keys for scalable access control of JPEG 2000 coded images," Proc. IEEE International Conference on Image Processing, pp. 3116–3119, Oct. 2008.
M. Tashiro, N. Hashimoto, Y. Murakami, T. Obi, M. Yamaguchi, N. Ohyama, and Y. Yagi, “Multispectral Whole Slide Imaging System,” Advancing Practice, Instruction and Innovation through Informatics, 2008.
N. Hashimoto, S. Nishino, T. Katsuoka, T. Shiraishi, D. Miwa, H. Hanada, J. Sakuma, H. Hontani, H. Miyoshi, and I. Takeuchi, “Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology,” arXiv:2609.31712, 2026. arXiv
H. Hanada, S. Akahane, N. Hashimoto, S. Takeno, and I. Takeuchi, “Safe Distributionally Robust Feature Selection under Covariate Shift,” arXiv:2603.16062, 2026. arXiv
K. Taguchi, K. Ohara, T. Yokota, H. Miyoshi, N. Hashimoto, I. Takeuchi, and H. Hontani, “Multi-Scale Representation of Follicular Lymphoma Pathology Images in a Single Hyperbolic Space,” arXiv:2506.18523, 2025. arXiv
D. Nishiyama, H. Miyoshi, N. Hashimoto, K. Ohshima, H. Hontani, I. Takeuchi, and J. Sakuma, “Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion,” arXiv:2503.00925, 2025. arXiv
S. Takeno, Y. Okura, Y. Inatsu, T. Aoyama, T. Tanaka, S. Akahane, H. Hanada, N. Hashimoto, T. Murayama, H. Lee, S. Kojima, and I. Takeuchi, “Distributionally Robust Active Learning for Gaussian Process Regression,” arXiv:2502.16870, 2025. arXiv
T. Aoyama, H. Yang, H. Hanada, S. Akahane, T. Tanaka, Y. Okura, Y. Inatsu, N. Hashimoto, T. Murayama, H. Lee, S. Kojima, and I. Takeuchi, “Generalized Kernel Inducing Points by Duality Gap for Dataset Distillation,” arXiv:2502.12607, 2025. arXiv
T. Tanaka, H. Hanada, H. Yang, T. Aoyama, Y. Inatsu, S. Akahane, Y. Okura, N. Hashimoto, T. Murayama, H. Lee, S. Kojima, and I. Takeuchi, “Distributionally Robust Coreset Selection under Covariate Shift,” arXiv:2501.14253, 2025. arXiv
H. Hanada, S. Akahane, T. Aoyama, T. Tanaka, Y. Okura, Y. Inatsu, N. Hashimoto, T. Murayama, H. Lee, S. Kojima, and I. Takeuchi, “Distributionally Robust Safe Screening,” arXiv:2404.16328, 2024. arXiv
J. Butke, N. Hashimoto, I. Takeuchi, H. Miyoshi, K. Ohshima, and J. Sakuma, “Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping,” arXiv:2310.12769, 2023. arXiv
H. Hanada, N. Hashimoto, K. Taji, and I. Takeuchi, “Generalized Low-Rank Update: Model Parameter Bounds for Low-Rank Training Data Modifications,” arXiv:2306.12670, 2023. arXiv
Y. Takagi, N. Hashimoto, H. Masuda, H. Miyoshi, K. Ohshima, H. Hontani, and I. Takeuchi, “Transformer-based Personalized Attention Mechanism for Medical Images with Clinical Records,” arXiv:2206.03003, 2022. arXiv
N. Hashimoto, Y. Takagi, H. Masuda, H. Miyoshi, K. Kohno, M. Nagaishi, K. Sato, M. Takeuchi, T. Furuta, K. Kawamoto, K. Yamada, M. Moritsubo, K. Inoue, Y. Shimasaki, Y. Ogura, T. Imamoto, T. Mishina, K. Tanaka, Y. Kawaguchi, S. Nakamura, K. Ohshima, H. Hontani, and I. Takeuchi, “Case-based Similar Image Retrieval for Weakly Annotated Large Histopathological Images of Malignant Lymphoma Using Deep Metric Learning,” arXiv:2107.03602, 2021. arXiv
N. Hashimoto, D. Fukushima, R. Koga, Y. Takagi, K. Ko, K. Kohno, M. Nakaguro, S. Nakamura, H. Hontani, and I. Takeuchi, “Multi-scale Domain-adversarial Multiple-instance CNN for Cancer Subtype Classification with Unannotated Histopathological Images,” arXiv:2001.01599, 2020. arXiv
K. Tanizaki, N. Hashimoto, Y. Inatsu, H. Hontani, and I. Takeuchi, “Computing Valid p-values for Image Segmentation by Selective Inference,” arXiv:1906.00629, 2019. arXiv
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