  Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively
with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy)
samples that do not conform to the expected normal patterns. UAD has two main
advantages over its fully supervised counterpart. Firstly, it is able to
directly leverage large datasets available from health screening programs that
contain mostly normal image samples, avoiding the costly manual labelling of
abnormal samples and the subsequent issues involved in training with extremely
class-imbalanced data. Further, UAD approaches can potentially detect and
localise any type of lesions that deviate from the normal patterns. One
significant challenge faced by UAD methods is how to learn effective
low-dimensional image representations to detect and localise subtle
abnormalities, generally consisting of small lesions. To address this
challenge, we propose a novel self-supervised representation learning method,
called Constrained Contrastive Distribution learning for anomaly detection
(CCD), which learns fine-grained feature representations by simultaneously
predicting the distribution of augmented data and image contexts using
contrastive learning with pretext constraints. The learned representations can
be leveraged to train more anomaly-sensitive detection models. Extensive
experiment results show that our method outperforms current state-of-the-art
UAD approaches on three different colonoscopy and fundus screening datasets.
Our code is available at https://github.com/tianyu0207/CCD.
