  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.
