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cs.CV2026
Statistical Testing for Multiple Instance Learning via Selective Inference with Applications to Computational Pathology
Noriaki Hashimoto, Shuichi Nishino, Teruyuki Katsuoka +7
Multiple instance learning (MIL) is widely used in computational pathology because it enables weakly supervised analysis of whole-slide images (WSIs) without requiring patch-level…
cs.CV2025
Multi-Scale Representation of Follicular Lymphoma Pathology Images in a Single Hyperbolic Space
Kei Taguchi, Kazumasa Ohara, Tatsuya Yokota +4
We propose a method for representing malignant lymphoma pathology images, from high-resolution cell nuclei to low-resolution tissue images, within a single hyperbolic space using s…
cs.CV2025
Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion
Daiki Nishiyama, Hiroaki Miyoshi, Noriaki Hashimoto +4
Malignant lymphoma subtype classification directly impacts treatment strategies and patient outcomes, necessitating classification models that achieve both high accuracy and suffic…