MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors
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...
| Authors | Bogdan Alexandru Bercean, Florinel Alin Croitoru, Vlad Hondru et al. |
|---|---|
| Primary category | cs.CV |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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