AI 中文总结
本研究提出架构感知评估协议，系统评估九种XAI技术在四类深度学习模型上的乳腺癌诊断解释质量，发现解释性能依赖模型与方法的联合选择，无单一技术通用。
AI 中文摘要
可解释人工智能（XAI）在医学图像分析中已成为确保基于深度学习（DL）的诊断系统透明性的关键。然而，针对乳腺癌识别选择合适的XAI技术在很大程度上仍是临时性的，缺乏跨不同深度学习架构的系统性评估。本研究提出了一种系统性的架构感知评估协议，以评估九种广泛使用的XAI技术在四类深度学习模型（极深、轻量、基于Transformer和混合神经网络）中的有效性。评估在包含780张图像的乳腺超声数据集上进行，使用临床对齐的空间指标，包括Pointing Game、Intersection over Union和Mean Coverage，以量化生成的解释与专家标注的病变区域之间的一致性。结果表明，解释质量主要受模型架构与XAI方法之间交互的影响，而非任何单一技术始终优于其他技术。混合架构产生空间上更连贯的解释，而轻量和基于Transformer的模型在不同方法间表现出更大的变异性。研究结果显示，没有任何单一技术能跨架构和评估标准泛化，强调需要联合选择深度学习模型和XAI技术。可解释性既取决于模型设计，也取决于解释策略，不应独立考虑。这项工作为乳腺癌诊断中选择XAI技术提供了结构化的评估协议和实用指导，支持更透明的临床决策支持系统。代码可在此https URL公开获取。
英文摘要
Explainable Artificial Intelligence (XAI) has become essential in medical image analysis to ensure transparency of deep learning (DL)-based diagnostic systems. However, selecting appropriate XAI techniques for breast cancer recognition remains largely ad hoc, with limited systematic evaluation across different DL architectures. This study presents a systematic architecture-aware evaluation protocol to assess the effectiveness of nine widely used XAI techniques across four categories of DL models: very deep, lightweight, transformer-based and hybrid neural networks. The evaluation is conducted on a breast ultrasound dataset comprising 780 images using clinically aligned spatial metrics, including Pointing Game, Intersection over Union and Mean Coverage, to quantify agreement between generated explanations and expert-annotated lesion regions. Results indicate that explanation quality is primarily influenced by the interaction between model architecture and XAI method, rather than any single technique consistently outperforming others. Hybrid architectures produce more spatially coherent explanations, while lightweight and transformer-based models exhibit greater variability across methods. The findings show that no single technique generalises across architectures and evaluation criteria, emphasising the need for joint selection of DL models and XAI techniques. Explainability depends on both model design and explanation strategy and should not be considered independently. This work provides a structured evaluation protocol and practical guidance for selecting XAI techniques in breast cancer diagnosis, supporting more transparent clinical decision-support systems. \textcolor{blue}{Code is publicly available at https://github.com/Nishan-Charlie/Explainable-AI}
Comments12 pages, 7 figures
