Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most methods utilize only known normal images during training and identify samples deviating from the normal profile as anomalies in the testing phase. Many readily available unlabeled images containing anomalies are thus ignored in the training phase, restricting the performance. To solve this problem, we introduce one-class semi-supervised learning (OC-SSL) to utilize known normal and unlabeled images for training, and propose Dual-distribution Discrepancy for Anomaly Detection (DDAD) based on this setting. Ensembles of reconstruction networks are designed to model the distribution of normal images and the distribution of both normal and unlabeled images, deriving the normative distribution module (NDM) and unknown distribution module (UDM). Subsequently, the intra-discrepancy of NDM and inter-discrepancy between the two modules are designed as anomaly scores. Furthermore, we propose a new perspective on self-supervised learning, which is designed to refine the anomaly scores rather than detect anomalies directly. Five medical datasets, including chest X-rays, brain MRIs and retinal fundus images, are organized as benchmarks for evaluation. Experiments on these benchmarks comprehensively compare a wide range of anomaly detection methods and demonstrate that our method achieves significant gains and outperforms the state-of-the-art. Code and organized benchmarks are available at https://github.com/caiyu6666/DDAD-ASR.

翻译：医学异常检测是一项关键且富有挑战性的任务，旨在识别异常图像以辅助诊断。由于异常图像的高标注成本，大多数方法仅在训练时使用已知的正常图像，并在测试阶段将与正常分布存在偏差的样本识别为异常。大量含有异常的未标注图像因此被忽略，从而限制了模型性能。为解决这一问题，我们引入单类半监督学习（OC-SSL）框架，利用已知正常图像和未标注图像进行训练，并提出基于该设置的双分布差异异常检测方法（DDAD）。通过设计重构网络集成学习正常图像分布以及正常与未标注图像联合分布，分别构建规范分布模块（NDM）和未知分布模块（UDM）。随后，设计NDM内部差异以及两模块之间的交叉差异作为异常分数。此外，我们提出自监督学习的新视角，旨在精炼异常分数而非直接检测异常。我们组织五个医学数据集（包括胸部X光、脑部MRI和视网膜眼底图像）作为基准评估。在这些基准上的实验全面比较了多种异常检测方法，表明我们的方法显著提升性能并超越当前最优水平。代码及组织基准已开源至 https://github.com/caiyu6666/DDAD-ASR。