发表机构
Lanzhou University; Shenzhen University; Southern Medical University; Central China Normal University（兰州大学; 深圳大学; 南方医科大学; 华中师范大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对乳腺超声诊断深度学习方法可解释性与鲁棒性不足的问题，提出训练时报告引导的TRACE框架，结合SCMT与BUSC基准，实现仅图像输入的高性能诊断与跨域鲁棒性提升。
AI 中文摘要
乳腺超声诊断依赖具有临床意义的语义概念，但大多数深度学习方法采用端到端的图像到标签范式，缺乏可解释性和鲁棒性。尽管基于概念的方法提供了有前景的替代方案，但它们通常假设存在完整标注，或在推理时需要多模态输入，这极大限制了其实际应用。为解决这些问题，我们提出了训练时报告引导且临床有序的概念编辑（TRACE），这是一种训练时报告引导的框架，利用结构化放射报告作为特权概念监督，同时支持测试时仅用图像进行诊断。TRACE在恶性感知的有序概念空间中，通过教师引导的编辑机制优化图像衍生的概念。为应对不完整标注问题，我们引入了策略性概念缺失训练（SCMT），并通过编辑蒸馏训练仅用图像的自编辑器以实现自主概念优化。此外，我们引入了概念丰富的基准BUSC，它将图像、标签和结构化属性关联起来。在多个数据集上的实验表明，与现有方法相比，TRACE实现了更优的性能和提升的跨域鲁棒性。
英文摘要
Breast ultrasound diagnosis relies on clinically meaningful semantic concepts, yet most deep learning methods adopt end-to-end image-to-label paradigms that lack interpretability and robustness. While concept-based approaches offer a promising alternative, they often assume complete annotations or require multimodal inputs at inference, which significantly limits their real-world applicability. To tackle these issues, we propose Training-time Report-guided and Clinically Ordered Concept Editing (TRACE), a training-time report-guided framework that leverages structured radiology reports as privileged concept supervision while enabling image-only diagnosis at test time. TRACE refines image-derived concepts through a teacher-guided editing mechanism within a malignancy-aware ordered concept space. To address incomplete annotations, we introduce Strategic Concept Missing Training (SCMT) and train an image-only self-editor via edit distillation for autonomous concept refinement. Besides, we introduce BUSC, a concept-enriched benchmark linking images, labels, and structured attributes. Experiments across multiple datasets demonstrate that TRACE achieves superior performance and improved cross-domain robustness compared to existing methods.
CommentsAccepted at the 34th ACM International Conference on Multimedia (ACM MM 2026). 9 pages, 3 figures Corrected typos and added references
