多任务学习提升医学异常检测，无需标注也能精准定位病灶。
MTL-MAD: Multi-Task Learners are Effective Medical Anomaly Detectors
- 用多个自监督任务联合训练，基于专家混合模型学习正常解剖结构。
- 在BMAD数据集上超越所有现有方法，异常检测准确率显著提升。
- 生成可解释的异常热力图，辅助医生诊断，适合临床应用。
医学图像中的异常检测极具挑战，因训练时通常缺乏异常样本。现有方法依赖单一预训练任务和大规模预训练模型达到先进性能。本文提出从零开始学习多个自监督与伪标签任务，采用基于专家混合（MoE）的联合模型。通过精心融合多个代理任务，该模型能有效学习正常解剖结构的鲁棒表征，推理时根据多任务学习器对各任务的解决能力生成异常评分。我们在涵盖多种医学影像模态的最新基准BMAD上进行了全面实验，结果表明，所提多任务学习器在异常检测上优于所有现有先进方法。此外，模型还能生成可解释的异常地图，有助于医生做出更准确的诊断。
原文摘要 · Abstract (English)
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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