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

TL;DR

This paper introduces a post-hoc normalizing flow-based method for out-of-distribution detection in medical imaging, enabling integration with pre-trained models without retraining, and demonstrates superior performance on relevant datasets.

Contribution

The authors propose a novel post-hoc normalizing flow approach for OOD detection that requires no model retraining, improving clinical reliability in medical imaging.

Findings

Achieves 84.61% AUROC on MedOOD, outperforming existing methods.

Reaches 93.8% AUROC on MedMNIST, surpassing prior models.

Provides a practical, easily integrable solution for clinical workflows.

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. We evaluate the approach on our in-house-curated MedOOD dataset, designed to capture clinically relevant distribution shifts, and on the MedMNIST benchmark. The proposed method achieves an AUROC of 84.61% on MedOOD, outperforming ViM (80.65%) and MDS (80.87%), and reaches 93.8% AUROC on MedMNIST, surpassing ViM (88.08%) and ReAct (87.05%). This combination of strong performance and post-hoc integration capability makes our approach a practical and effective safeguard for clinical imaging workflows. The model and code to build OOD datasets are publicly accessible at https://github.com/dlotfi/MedOODFlow.