MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors

• URL: http://arxiv.org/abs/2605.05891v1
• Date: Thu, 07 May 2026 09:03:09 GMT
• Title: MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors
• Abstract summary: Anomaly detection in medical images is a challenging task, since anomalies are not typically available during training.<n>We propose to learn multiple self-supervised and pseudo-labeling tasks from scratch, using a joint model based on Mixture-of-Experts (MoE)<n>Our model produces interpretable anomaly maps, potentially helping physicians in providing more accurate diagnoses.
• Score: 22.46953076078191
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Anomaly detection in medical images is a challenging task, since anomalies are not typically available during training. Recent methods leverage a single pretext task coupled with a large-scale pre-trained model to reach state-of-the-art performance. Instead, we propose to learn multiple self-supervised and pseudo-labeling tasks from scratch, using a joint model based on Mixture-of-Experts (MoE). By carefully integrating multiple proxy tasks, the joint model effectively learns a robust representation of normal anatomical structures, so that anomaly scores can be derived based on how well the multi-task learner (MTL) solves each task during inference. We perform comprehensive experiments on BMAD, a recent benchmark that comprises a broad range of medical image modalities. The empirical results indicate that our multi-task learner is an effective anomaly detector, outperforming all state-of-the-art competitors on BMAD. Moreover, our model produces interpretable anomaly maps, potentially helping physicians in providing more accurate diagnoses.

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