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This paper introduces MARVEL, a robust framework for out-of-distribution (OOD) detection in clinical settings, addressing the limitations of existing methods that often rely on balanced datasets and simplistic evaluation scenarios. By employing a Nonlinear von Mises-Fisher classifier within a multi-expert architecture, MARVEL effectively learns non-linear decision boundaries and specializes in handling imbalanced label distributions. The framework demonstrates significant improvements over state-of-the-art methods, achieving mean false positive rate reductions of up to 36.90% across diverse medical datasets, thereby enhancing the reliability of AI-assisted diagnostics in real-world applications.
Achieving up to 36.90% reduction in false positive rates, MARVEL transforms OOD detection for clinical AI systems by effectively addressing data imbalance and unseen cases.
For clinical deployment, it is essential that automated diagnostic systems remain reliable when confronted with previously unseen cases, yet deep models routinely misclassify out-of-distribution (OOD) inputs with high confidence, underscoring the need for more robust OOD detection methods. Although substantial effort has been devoted to improving model robustness, most of the existing literature assumes balanced datasets, evaluates OOD detection on coarse or non-clinical OOD sources, or lacks comprehensive assessment across diverse OOD scenarios. To address the gaps, we propose a novel methodology trained on diverse and imbalanced medical datasets and evaluated across a clinically reflective OOD spectrum. Our framework comprises three key components: (1) a Nonlinear von Mises-Fisher (NvMF) classifier capable of learning non-linear decision boundaries, with theoretical proof of its asymptotic connection to cosine classifiers; (2) a multi-expert framework in which margin-aware NvMF classifiers specialise in different regions of label distribution to better handle imbalance; and (3) an outlier expert trained explicitly to distinguish inlier from outlier data, thereby strengthening OOD detection. Evaluation on RFMiD, ISIC2019, and NCTCRC datasets demonstrates consistent improvements over state-of-the-art methods, achieving mean FPR95 reductions of 8.45%, 13.02%, and 36.90% respectively. These gains are further supported by comprehensive ablations that validated the contributions of each component. This enables reliable identification of unfamiliar cases for deferral to clinicians, supporting safer AI-assisted diagnosis in real-world workflows. Our code is available at https://github.com/redboxup/MARVEL.
