TRACE: Training-time Report-guided and Clinically Ordered Concept Editing

• URL: http://arxiv.org/abs/2608.20809v1
• Date: Fri, 21 Aug 2026 07:26:39 GMT
• Title: TRACE: Training-time Report-guided and Clinically Ordered Concept Editing
• Abstract summary: We propose Training-time Report-guided and Clinically Ordered Concept Editing (TRACE)<n>TRACE refines image-derived concepts through a teacher-guided editing mechanism within a malignancy-aware ordered concept space.<n>We introduce Strategic Concept Missing Training (SCMT) and train an image-only self-editor via edit distillation for autonomous concept refinement.
• Score: 9.375094449682043
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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