Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion
Journal:
arXiv
Published Date:
Mar 2, 2025
Abstract
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.
