DCG-Net：双交叉注意力与概念-值图推理用于可解释的医学诊断
DCG-Net: Dual Cross-Attention with Concept-Value Graph Reasoning for Interpretable Medical Diagnosis
- School of Information and Communication Technology（信息与通信技术学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
DCG-Net通过双交叉注意力和概念-值图推理，提升医学诊断的可解释性，实现白血球形态和皮肤病变诊断的高精度分类。
AI中文摘要：
深度学习模型在医学图像分析中表现优异，但其内部决策过程难以解释。概念瓶颈模型（CBMs）通过人类可解释的临床概念结构化预测，但现有CBMs通常忽略概念间的上下文依赖。为此，我们提出端到端可解释框架DCG-Net，整合多模态对齐与结构化概念推理。DCG-Net引入双交叉注意力模块，用视觉token与标准化文本概念-值原型间的双向注意力替代余弦相似度匹配，实现空间局部证据归因。为捕捉临床概念的内在关系结构，我们开发了参数化概念图，初始时使用正点互信息先验，通过稀疏控制的消息传递进行细化。此方法以与临床领域知识一致的方式建模概念间依赖。在白血球形态和皮肤病变诊断实验中，DCG-Net实现了最先进的分类性能，同时产生临床可解释的诊断解释。
英文摘要：
Deep learning models have achieved strong performance in medical image analysis, but their internal decision processes remain difficult to interpret. Concept Bottleneck Models (CBMs) partially address this limitation by structuring predictions through human-interpretable clinical concepts. However, existing CBMs typically overlook the contextual dependencies among concepts. To address these issues, we propose an end-to-end interpretable framework \emph{DCG-Net} that integrates multimodal alignment with structured concept reasoning. DCG-Net introduces a Dual Cross-Attention module that replaces cosine similarity matching with bidirectional attention between visual tokens and canonicalized textual concept-value prototypes, enabling spatially localized evidence attribution. To capture the relational structure inherent to clinical concepts, we develop a Parametric Concept Graph initialized with Positive Pointwise Mutual Information priors and refined through sparsity-controlled message passing. This formulation models inter-concept dependencies in a manner consistent with clinical domain knowledge. Experiments on white blood cell morphology and skin lesion diagnosis demonstrate that DCG-Net achieves state-of-the-art classification performance while producing clinically interpretable diagnostic explanations.
