  We introduce a neural network framework, utilizing adversarial learning to
partition an image into two cuts, with one cut falling into a reference
distribution provided by the user. This concept tackles the task of
unsupervised anomaly segmentation, which has attracted increasing attention in
recent years due to their broad applications in tasks with unlabelled data.
This Adversarial-based Selective Cutting network (ASC-Net) bridges the two
domains of cluster-based deep learning methods and adversarial-based
anomaly/novelty detection algorithms. We evaluate this unsupervised learning
model on BraTS brain tumor segmentation, LiTS liver lesion segmentation, and
MS-SEG2015 segmentation tasks. Compared to existing methods like the AnoGAN
family, our model demonstrates tremendous performance gains in unsupervised
anomaly segmentation tasks. Although there is still room to further improve
performance compared to supervised learning algorithms, the promising
experimental results shed light on building an unsupervised learning algorithm
using user-defined knowledge.
