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arXiv · 2603.05534

In-batch Relational Features Enhance Precision in An Unsupervised Medical Anomaly Detection Task

P. Bilha Githinji·Ijaz Gul·Lian Zhang·Jinhao Xu·Peiwu Qin·Dongmei Yu
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Abstract

Confounding pathology with normal anatomical variation remains a significant challenge in unsupervised medical-image anomaly detection, resulting in numerous false positives. To enhance integration of healthy variation, we augment the latent representation of a CNN autoencoder with contextual similarities within a normal cohort through batch-wise hypergraph estimation and a shared-weights graph convolution layer, producing a population-aware embedding. On a heterogeneous brain-tumor dataset of 2D MRI scans, the method improves separability between healthy and pathological samples, achieving an AUC-ROC of 0.90 (95% CI 0.84-0.95, 5.7% absolute gain), and a 16% absolute improvement in average precision (0.78 AP, 95% CI 0.66-0.89), thereby lowering false-positive rates. Moreover, both anomaly detection and downstream tumor versus no-tumor classification performance improve with the size of the mini-batch context captured in the augmented representation, suggesting a tunable lever for integrating healthy variation.

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P. Bilha Githinji, Ijaz Gul, Lian Zhang, Jinhao Xu, Peiwu Qin, Dongmei Yu. 2026-03-03. In-batch Relational Features Enhance Precision in An Unsupervised Medical Anomaly Detection Task. https://arxiv.org/abs/2603.05534

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