@article{Wang2026, 
author = {Xuerong Wang and Jiarui Wang and Le Yang and Yao Tian and Meiyue Song and Binglu Wang},
title = {ProACT: prototype-guided anchor-based test-time adaptation for pneumoconiosis diagnosis},
year = {2026},
journal = {Visual Intelligence},
volume = {4},
pages = {25},
keywords = {Pneumoconiosis diagnosis, Multi-center deployment, Test-time adaptation (TTA), Prototype-based learning},
url = {https://www.sciopen.com/article/10.1007/s44267-026-00128-y},
doi = {10.1007/s44267-026-00128-y},
abstract = {Maintaining reliable performance in the multi-center deployment of pneumoconiosis diagnosis models is difficult under severe distribution shifts because adaptation must respect the multi-region diagnostic criteria specific to pneumoconiosis. Generic grounded test-time adaptation (TTA) methods are primarily designed for unconstrained classification settings, and their direct application to pneumoconiosis diagnosis can cause semantic drift when there are severe cross-center shifts. In this paper, we propose the ProACT framework, which is a clinically grounded, prototype-guided, anchor-based TTA for stable, multi-center pneumoconiosis diagnosis using fully unlabeled target data. ProACT addresses the challenges of multi-center pneumoconiosis diagnosis using a prototype-guided, anchor-based stabilization mechanism and a sensitivity-aware parameter adaptation strategy. The former uses clinically validated reference radiographs to assess the reliability of the target samples and guide the construction of multiple prototypes, explicitly modeling cross-center intra-class heterogeneity. The latter selectively updates the loss-sensitive components of the feature extractor to preserve the clinically meaningful diagnostic logic. Experiments on a large-scale, real-world multi-center pneumoconiosis benchmark collected from multiple hospitals demonstrate that ProACT consistently outperforms existing deployment-time adaptation methods. ProACT achieves the highest level of agreement with radiologists, and maintains stable performance across different network architectures.}
}