1 citations · 1 across the 2 of their papers we have counts for
3 papers · 1 filter
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…
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…
Mixing Histopathology Prototypes into Robust Slide-Level Representations for Cancer Subtyping
Joshua Butke, Noriaki Hashimoto, Ichiro Takeuchi +3
Whole-slide image analysis via the means of computational pathology often relies on processing tessellated gigapixel images with only slide-level labels available. Applying multipl…