Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows

• URL: http://arxiv.org/abs/2502.11638v2
• Date: Wed, 28 May 2025 18:40:26 GMT
• Title: Safeguarding AI in Medical Imaging: Post-Hoc Out-of-Distribution Detection with Normalizing Flows
• Authors: Dariush Lotfi, Mohammad-Ali Nikouei Mahani, Mohamad Koohi-Moghadam, Kyongtae Ty Bae,
• Abstract summary: Current OOD detection methods demand impractical retraining or modifications to pre-trained models.<n>We propose a post-hoc normalizing flow-based approach that seamlessly integrates with existing pre-trained models.<n>Our method achieved an AUROC of 84.61%, outperforming state-of-the-art methods like ViM (80.65%) and MDS (80.87%)
• Score: 3.3968168503957625
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In AI-driven medical imaging, the failure to detect out-of-distribution (OOD) data poses a severe risk to clinical reliability, potentially leading to critical diagnostic errors. Current OOD detection methods often demand impractical retraining or modifications to pre-trained models, hindering their adoption in regulated clinical environments. To address this challenge, we propose a post-hoc normalizing flow-based approach that seamlessly integrates with existing pre-trained models without altering their weights. Our evaluation used a novel in-house built dataset, MedOOD, meticulously curated to simulate clinically relevant distributional shifts, alongside the MedMNIST benchmark dataset. On our in-house MedOOD dataset, our method achieved an AUROC of 84.61%, outperforming state-of-the-art methods like ViM (80.65%) and MDS (80.87%). Similarly, on MedMNIST, it reached an exceptional AUROC of 93.8%, surpassing leading approaches such as ViM (88.08%) and ReAct (87.05%). This superior performance, coupled with its post-hoc integration capability, positions our method as a vital safeguard for enhancing safety in medical imaging workflows. The model and code to build OOD datasets are publicly accessible at https://github.com/dlotfi/MedOODFlow.

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