利用病理学共现进行胸部X光诊断的测试时适应
Leveraging Pathology Co-occurrence for Test-Time Adaptation in Chest X-Ray Diagnosis
- Seoul National University（首尔国立大学）
- Nanyang Technological University（南洋理工大学）
- UNIST（蔚山科学技术院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对医学影像模型在新临床地点性能下降问题，提出共现加权适应（CoWA）方法，利用疾病共现模式作适应可靠性信号，估计标签共现结构并降低偏离模式样本权重，在胸部X光基准测试中优于基线。
AI中文摘要：
医学成像模型在新临床地点部署时常因成像设备、协议和患者群体差异而性能下降。测试时适应（TTA）通过仅使用未标记目标数据更新预训练模型来解决此问题。现有TTA方法针对自然图像基准的单标签分类设计，未考虑标签依赖性。本文提出CoWA，利用疾病共现模式作为适应的可靠性信号。CoWA从模型预测中估计标签共现结构，对偏离预期模式的样本加权，使适应更多依赖一致预测，减少噪声影响。我们在域转移下的胸部X光基准上评估CoWA，并证明其优于既定基线。
英文摘要：
Medical imaging models often degrade when deployed at new clinical sites due to differences in imaging equipment, protocols, and patient populations. Test-time adaptation (TTA) addresses this by updating a pretrained model using only unlabeled target data, without access to source data. However, existing TTA methods were designed for single-label classification on natural image benchmarks, minimizing entropy uniformly across all samples without considering label dependencies. This overlooks a key property of multi-label medical imaging: pathologies do not occur independently but exhibit structured co-occurrence patterns. In this work, we propose Co-occurrence Weighted Adaptation (CoWA), which leverages disease co-occurrence patterns as a reliability signal for adaptation. CoWA estimates label co-occurrence structure from model predictions and downweights samples that deviate from expected patterns, enabling adaptation to rely more on consistent predictions while reducing the impact of noisy ones. We evaluate CoWA on chest X-ray benchmarks under domain shifts and demonstrate consistent improvements over established baselines.
