Abstract
Accurate classification of breast cancer histopathology images is pivotal for early oncological diagnosis and therapeutic this http URL, conventional deep learning architectures often encounter performance degradation under limited annotations and suffer from a "blackbox" nature, hindering their clinical integration. To mitigate these limitations, we propose GAFRNet, a robust and interpretable Graph Attention and FuzzyRule Network specifically engineered for histopathology image classification with scarce supervision. GAFRNet constructs a similarity-driven graph representation to model intersample relationships and employs a multihead graph attention mechanism to capture complex relational features across heterogeneous tissue this http URL, a differentiable fuzzy-rule module encodes intrinsic topological descriptorsincluding node degree, clustering coefficient, and label consistencyinto explicit, human-understandable diagnostic logic. This design establishes transparent "IF-THEN" mappings that mimic the heuristic deduction process of medical experts, providing clear reasoning behind each prediction without relying on post-hoc attribution methods. Extensive evaluations on three benchmark datasets (BreakHis, Mini-DDSM, and ICIAR2018) demonstrate that GAFR-Net consistently outperforms various state-of-the-art methods across multiple magnifications and classification tasks. These results validate the superior generalization and practical utility of GAFR-Net as a reliable decision-support tool for weakly supervised medical image analysis.
Abstract (translated)
乳腺癌组织病理图像的准确分类对于早期肿瘤诊断和治疗至关重要。然而，传统的深度学习架构在标注数据有限的情况下性能会下降，并且由于其“黑箱”特性而难以被临床应用。为了解决这些问题，我们提出了一种名为GAFRNet（Graph Attention and Fuzzy Rule Network）的强大且可解释的网络，专门用于稀缺监督条件下的组织病理图像分类。GAFRNet通过构建以相似性驱动的图表示来建模样本间的相互关系，并采用多头图注意力机制来捕捉异质组织中的复杂关联特征。此外，一个可微分模糊规则模块将内在拓扑描述符（包括节点度、聚类系数和标签一致性）编码为明确的人类易于理解的诊断逻辑。这种设计建立了透明的“如果-那么”映射关系，模拟了医疗专家基于直觉进行推断的过程，并且无需依赖事后归因方法就能提供清晰的推理依据。 在三个基准数据集（BreakHis、Mini-DDSM和ICIA2018）上的广泛评估表明，GAFRNet在多种放大倍率和分类任务中均超越了各种最新的方法。这些结果验证了GAFR-Net作为弱监督医学影像分析可靠决策支持工具的优越泛化能力和实用价值。 通过这种方式，GAFRNet不仅能够提高诊断准确性，还能增强医生对模型输出的理解与信任度，从而促进其在临床环境中的广泛采用。
URL
https://arxiv.org/abs/2602.09318
