  Chest X-ray (CXR) is the most typical radiological exam for diagnosis of
various diseases. Due to the expensive and time-consuming annotations,
detecting anomalies in CXRs in an unsupervised fashion is very promising.
However, almost all of the existing methods consider anomaly detection as a
one-class classification (OCC) problem. They model the distribution of only
known normal images during training and identify the samples not conforming to
normal profile as anomalies in the testing phase. A large number of unlabeled
images containing anomalies are thus ignored in the training phase, although
they are easy to obtain in clinical practice. In this paper, we propose a novel
strategy, Dual-distribution Discrepancy for Anomaly Detection (DDAD), utilizing
both known normal images and unlabeled images. The proposed method consists of
two modules. During training, one module takes both known normal and unlabeled
images as inputs, capturing anomalous features from unlabeled images in some
way, while the other one models the distribution of only known normal images.
Subsequently, inter-discrepancy between the two modules, and intra-discrepancy
inside the module that is trained on only normal images are designed as anomaly
scores to indicate anomalies. Experiments on three CXR datasets demonstrate
that the proposed DDAD achieves consistent, significant gains and outperforms
state-of-the-art methods. Code is available at
https://github.com/caiyu6666/DDAD.
