Abstract
Unsupervised anomaly detection (UAD) methods are trained with normal (or
healthy) images only, but during testing, they are able to classify normal and
abnormal (or disease) images. UAD is an important medical image analysis (MIA)
method to be applied in disease screening problems because the training sets
available for those problems usually contain only normal images. However, the
exclusive reliance on normal images may result in the learning of ineffective
low-dimensional image representations that are not sensitive enough to detect
and segment unseen abnormal lesions of varying size, appearance, and shape.
Pre-training UAD methods with self-supervised learning, based on computer
vision techniques, can mitigate this challenge, but they are sub-optimal
because they do not explore domain knowledge for designing the pretext tasks,
and their contrastive learning losses do not try to cluster the normal training
images, which may result in a sparse distribution of normal images that is
ineffective for anomaly detection. In this paper, we propose a new
self-supervised pre-training method for MIA UAD applications, named Pseudo
Multi-class Strong Augmentation via Contrastive Learning (PMSACL). PMSACL
consists of a novel optimisation method that contrasts a normal image class
from multiple pseudo classes of synthesised abnormal images, with each class
enforced to form a dense cluster in the feature space. In the experiments, we
show that our PMSACL pre-training improves the accuracy of SOTA UAD methods on
many MIA benchmarks using colonoscopy, fundus screening and Covid-19 Chest
X-ray datasets. The code is made publicly available via
https://github.com/tianyu0207/PMSACL.