演化错误状态：面向超声病灶分割的故障感知渐进修复

中文摘要 AI 辅助

在稀疏且异质的故障下保持可靠性，仍然是医学图像分割面临的基本挑战。高平均准确率可能掩盖一小部分结构上独特且临床后果严重的错误。现有的后处理修正方法缓解了这一问题，但通常从同一固定预测中估计假阳性和假阴性修正，这忽略了错误状态的动态演化，并限制了复杂病例的修正能力。受结构化预测中迭代错误反馈的启发，我们提出了故障感知渐进修复（FAPR）。FAPR将当前分割掩码表示为动态故障状态，并将每次修复操作建模为状态转移算子。每次被接受的修正都会形成新的预测状态，供后续错误诊断和修复使用，从而使后续操作能够适应先前的变化。条件路由选择性地激活必要的状态转移，而故障重放则使模型暴露于罕见的错误状态。通过保持基础分割器冻结，FAPR在改善困难病例的同时，保留了其既有的分割能力。在三个公开的超声病灶分割基准上，FAPR将平均DSC提高了1.52%。在BUSI和TN3K的极难子集上，平均增益达到13.77%。

英文摘要

Reliability under sparse and heterogeneous failures remains a fundamental challenge for medical image segmentation. High average accuracy can conceal a small set of structurally distinct and clinically consequential errors. Existing post-hoc correction methods alleviate this problem, but typically estimate false-positive and false-negative corrections from the same fixed prediction. This ignores the dynamic evolution of error states and limits the correction of complex cases. Inspired by iterative error feedback in structured prediction, we propose Failure-Aware Progressive Repair (FAPR). FAPR represents the current segmentation mask as a dynamic failure state and models each repair operation as a state-transition operator. Each accepted correction forms a new prediction state for subsequent error diagnosis and repair, enabling later operations to adapt to preceding changes. Conditional routing selectively activates necessary state transitions, while failure replay exposes the model to rare error states. By keeping the base segmentor frozen, FAPR preserves its established segmentation capability while improving difficult cases. Across three public ultrasound lesion segmentation benchmarks, FAPR improves mean DSC by 1.52%. On the very-hard subsets of BUSI and TN3K, the average gain reaches 13.77%.

发表机构

• Central South University（中南大学）
• Yangtze University（长江大学）
• Chongqing University（重庆大学）
• Peking University（北京大学）
• Southwest Jiaotong University（西南交通大学）

机构由 AI 辅助整理，请以论文原文为准。