融合细胞图与图像信息，实现淋巴瘤亚型分类的高精度可解释分析
Explainable Classifier for Malignant Lymphoma Subtyping via Cell Graph and Image Fusion
- 通过细胞图与图像特征融合，识别亚型特异性区域
- 在1233张全切片图像上达到顶尖分类准确率
- 提供病理科医生认可的区域与细胞级解释
恶性淋巴瘤亚型分类直接影响治疗策略和患者预后，因此需要兼具高准确率与强可解释性的分类模型。本文提出一种新颖的可解释多实例学习（MIL）框架，从全切片图像（WSIs）中识别出亚型特异性的感兴趣区域（ROIs），并整合细胞分布特征与图像信息。该框架同时实现三个目标：(1) 为每种亚型指明合适的ROI；(2) 解释特征细胞类型的频率与空间分布；(3) 通过融合图像与细胞分布模态实现高精度亚型分类。方法采用混合专家（MoE）机制融合每个图像块中的细胞图与图像特征，并在MIL框架下进行分类。在包含1,233张全切片图像的数据集上，本方法在十种对比方法中表现最优，且生成的区域级与细胞级解释与病理医生观点一致。
原文摘要 · Abstract (English)
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.
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