Malignant lymphoma subtype classification directly impacts treatment strategies and patient outcomes, necessitating classification models that achieve both high accuracy and sufficient explainability. This study proposes a novel explainable Multi-Instance Learning (MIL) framework that identifies subtype-specific Regions of Interest (ROIs) from Whole Slide Images (WSIs) while integrating cell distribution characteristics and image information. Our framework simultaneously addresses three objectives: (1) indicating appropriate ROIs for each subtype, (2) explaining the frequency and spatial distribution of characteristic cell types, and (3) achieving high-accuracy subtyping by leveraging both image and cell-distribution modalities. The proposed method fuses cell graph and image features extracted from each patch in the WSI using a Mixture-of-Experts (MoE) approach and classifies subtypes within an MIL framework. Experiments on a dataset of 1,233 WSIs demonstrate that our approach achieves state-of-the-art accuracy among ten comparative methods and provides region-level and cell-level explanations that align with a pathologist's perspectives.
@article{arxiv.2503.00925,
title = {Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion},
author = {Daiki Nishiyama and Hiroaki Miyoshi and Noriaki Hashimoto and Koichi Ohshima and Hidekata Hontani and Ichiro Takeuchi and Jun Sakuma},
journal= {arXiv preprint arXiv:2503.00925},
year = {2025}
}