OOD-aware Reliability Learning for open-world semi-supervised liver lesion recognition.

Abstract

Prelocalized focal liver lesion classification from multi-phase magnetic resonance imaging remains challenging in multi-center clinical practice because lesion-level annotation is limited, imaging protocols vary across institutions, and routine unlabeled data may contain categories outside the predefined training taxonomy. Existing semi-supervised methods usually assume a closed label space and can therefore be affected by unreliable pseudo-labels when unknown or weakly supported lesions appear in the unlabeled pool. These challenges are closely related to out-of-distribution (OOD) effects caused by category mismatch and clinical distribution shift. To address this problem, we propose OOD-aware Reliability Learning (ORL), a semi-supervised framework for prelocalized liver lesion classification under sparse annotation, partial label-space mismatch, and multi-center distribution shift. ORL learns a compact known-class reference manifold using Prototype-Constrained Representation Learning (PCRL), estimates sample-wise manifold support through Transport-derived Compatibility Estimation (TCE) with Asymmetric Relaxed Optimal Transport (AROT), and combines this support with classifier confidence and prototype affinity to regulate pseudo-label learning. The framework also learns an amortized reliability predictor for inference-time support scoring and reliability-based case ranking after lesion localization. We evaluated ORL on a multi-center liver magnetic resonance imaging cohort and selected retrospective stress settings, including external-center evaluation, unlabeled-pool contamination, missing-phase testing, and composite OOD-oriented score analyses. ORL improved known-category classification over representative semi-supervised baselines and maintained better performance under contamination and distribution shift. These results indicate that manifold support estimation can improve label-efficient prelocalized liver lesion classification and may improve reliability-based case ranking after lesion localization in the evaluated retrospective setting.