Explaining the decisions of Deep Neural Networks (DNNs) for medical images has become increasingly important. Existing attribution methods have difficulty explaining the meaning of pixels while existing concept-based methods are limited by additional annotations or specific model structures that are difficult to apply to ultrasound images. In this paper, we propose the Lesion Concept Explainer (LCE) framework, which combines attribution methods with concept-based methods. We introduce the Segment Anything Model (SAM), fine-tuned on a large number of medical images, for concept discovery to enable a meaningful explanation of ultrasound image DNNs. The proposed framework is evaluated in terms of both faithfulness and understandability. We point out deficiencies in the popular faithfulness evaluation metrics and propose a new evaluation metric. Our evaluation of public and private breast ultrasound datasets (BUSI and FG-US-B) shows that LCE performs well compared to commonly-used explainability methods. Finally, we also validate that LCE can consistently provide reliable explanations for more meaningful fine-grained diagnostic tasks in breast ultrasound.

本研究解决了现有深度神经网络（DNN）在超声图像解释中的局限性，提出了病灶概念解释器（LCE）框架，结合归因方法与概念基础方法以改进可解释性。通过引入针对医学图像的“随意分割模型”（SAM），该框架在各类超声图像数据集上表现优异，提供更可信赖和可理解的解释，尤其在细粒度诊断任务中具有重要影响。

基于概念发现的超声图像深度神经网络可解释性框架