EviATTA：基于证据的主动测试时间适应用于医学分割任何模型
EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Models
AI总结：
本文提出EviATTA框架，通过证据建模和分层采样策略提升医学SAMs在测试时适应的可靠性，结合双重一致性正则化以更有效利用稀疏标注。
摘要：
在大规模分布偏移下，通过测试时间适应（TTA）部署基础医学分割任何模型（SAMs）具有挑战性，因为测试时间监督通常不可靠。尽管主动测试时间适应（ATTA）引入了有限的专家反馈以提高可靠性，但现有方法仍存在不确定估计不可靠和稀疏标注利用效率低的问题。为了解决这些问题，我们提出了证据主动测试时间适应（EviATTA），这是我们所知的第一个针对医学SAMs的ATTA框架。具体而言，我们采用基于Dirichlet的证据建模，将总体预测不确定性分解为分布不确定性和数据不确定性。在此分解基础上，我们设计了一种分层证据采样策略，其中图像层面的分布不确定性用于选择信息丰富的偏移样本，而距离感知的数据不确定性指导稀疏像素标注以解决数据歧义。我们进一步引入了双重一致性正则化，该正则化在稀疏标注样本上强制逐步提示一致性以更好地利用稀疏监督，并在未标注样本上应用变分特征一致性以稳定适应。在六个医学图像分割数据集上的广泛实验表明，EviATTA在批量和实例级测试时间适应设置下，通过最小的专家反馈一致地提高了适应可靠性。
英文摘要：
Deploying foundational medical Segment Anything Models (SAMs) via test-time adaptation (TTA) is challenging under large distribution shifts, where test-time supervision is often unreliable. While active test-time adaptation (ATTA) introduces limited expert feedback to improve reliability, existing ATTA methods still suffer from unreliable uncertainty estimation and inefficient utilization of sparse annotations. To address these issues, we propose Evidential Active Test-Time Adaptation (EviATTA), which is, to our knowledge, the first ATTA framework tailored for medical SAMs. Specifically, we adopt the Dirichlet-based Evidential Modeling to decompose overall predictive uncertainty into distribution uncertainty and data uncertainty. Building on this decomposition, we design a Hierarchical Evidential Sampling strategy, where image-wise distribution uncertainty is used to select informative shifted samples, while distance-aware data uncertainty guides sparse pixel annotations to resolve data ambiguities. We further introduce Dual Consistency Regularization, which enforces progressive prompt consistency on sparsely labeled samples to better exploit sparse supervision and applies variational feature consistency on unlabeled samples to stabilize adaptation. Extensive experiments on six medical image segmentation datasets demonstrate that EviATTA consistently improves adaptation reliability with minimal expert feedback under both batch-wise and instance-wise test-time adaptation settings.
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。
