LCE：一种基于概念发现的超声图像深度神经网络可解释性框架
LCE: A Framework for Explainability of DNNs for Ultrasound Image Based on Concept Discovery
AI总结：
针对现有医学图像DNN可解释性方法难以适配超声图像的问题，提出结合归因与概念发现的LCE框架，引入微调的SAM挖掘概念，提出新评估指标，在乳腺超声数据集上表现优于现有方法且可支持细粒度诊断解释。
摘要：
解释深度神经网络（DNNs）针对医学图像的决策已变得愈发重要。现有归因方法难以解释像素的含义，而现有基于概念的方法受限于额外标注或特定模型结构，难以应用于超声图像。本文提出病灶概念解释器（Lesion Concept Explainer，LCE）框架，将归因方法与基于概念的方法相结合。我们引入在大量医学图像上微调的Segment Anything Model（SAM，分割一切模型）用于概念发现，以实现对超声图像DNN的有意义解释。我们从忠实度和可理解性两方面对所提框架进行评估，指出了常用忠实度评估指标的不足，并提出了一种新的评估指标。我们在公开和私有乳腺超声数据集（BUSI和FG-US-B）上的评估表明，与常用的可解释性方法相比，LCE表现优异。最后，我们还验证了LCE能够为乳腺超声中更具意义的细粒度诊断任务持续提供可靠的解释。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
