[2603.00201] AdURA-Net: Adaptive Uncertainty and Region-Aware Network

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00201 (cs) [Submitted on 27 Feb 2026] Title:AdURA-Net: Adaptive Uncertainty and Region-Aware Network Authors:Antik Aich Roy, Ujjwal Bhattacharya View a PDF of the paper titled AdURA-Net: Adaptive Uncertainty and Region-Aware Network, by Antik Aich Roy and Ujjwal Bhattacharya View PDF HTML (experimental) Abstract:One of the common issues in clinical decision-making is the presence of uncertainty, which often arises due to ambiguity in radiology reports, which often reflect genuine diagnostic uncertainty or limitations of automated label extraction in various complex cases. Especially the case of multilabel datasets such as CheXpert, MIMIC-CXR, etc., which contain labels such as positive, negative, and uncertain. In clinical decision-making, the uncertain label plays a tricky role as the model should not be forced to provide a confident prediction in the absence of sufficient evidence. The ability of the model to say it does not understand whenever it is not confident is crucial, especially in the cases of clinical decision-making involving high risks. Here, we propose AdURA-Net, a geometry-driven adaptive uncertainty-aware framework for reliable thoracic disease classification. The key highlights of the proposed model are: a) Adaptive dilated convolution and multiscale deformable alignment coupled with the backbone Densenet architecture capturing the anatomical complexities of the medical images, and b) D...