ASC-Net: Unsupervised Medical Anomaly Segmentation Using an Adversarial-based Selective Cutting Network. (arXiv:2112.09135v1 [eess.IV])

In this paper we consider the problem of unsupervised anomaly segmentation in
medical images, which has attracted increasing attention in recent years due to
the expensive pixel-level annotations from experts and the existence of a large
amount of unannotated normal and abnormal image scans. We introduce a
segmentation network that utilizes adversarial learning to partition an image
into two cuts, with one of them falling into a reference distribution provided
by the user. This Adversarial-based Selective Cutting network (ASC-Net) bridges
the two domains of cluster-based deep segmentation and adversarial-based
anomaly/novelty detection algorithms. Our ASC-Net learns from normal and
abnormal medical scans to segment anomalies in medical scans without any masks
for supervision. We evaluate this unsupervised anomly segmentation model on
three public datasets, i.e., BraTS 2019 for brain tumor segmentation, LiTS for
liver lesion segmentation, and MS-SEG 2015 for brain lesion segmentation, and
also on a private dataset for brain tumor segmentation. Compared to existing
methods, 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 and interesting observations shed light on building an
unsupervised learning algorithm for medical anomaly identification using
user-defined knowledge.



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