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Title: ASC-Net: Adversarial-Based Selective Network for Unsupervised Anomaly Segmentation
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.  more » « less
Award ID(s):
1755970
NSF-PAR ID:
10350888
Author(s) / Creator(s):
;
Date Published:
Journal Name:
International Conference on Medical Image Computing and Computer-Assisted Intervention
Page Range / eLocation ID:
236-247
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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