Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification

ArXi:2508.04457v2 Announce Type: replace-cross Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neural networks to quantify predictive, epistemic, and aleatoric uncertainties using an information-theoretical approach in synthetic or well defined data settings like natural image classification, its applicability to real life medical diagnosis tasks remains underexplored.