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Consistency and Adversarial Semi-supervised Learning for Medical Image Segmentation by Yongqiang Tang; Shilei Wang; Yuxun Qu; Zhihua Cui; Wensheng Zhang is a Medicine article available to read on EtoBox.

What is Consistency and Adversarial Semi-supervised Learning for Medical Image Segmentation about?

Medical image segmentation based on deep learning has made enormous progress in recent years. However, the performance of existing methods generally heavily relies on a large amount of labeled data, which are commonly expensive and time-consuming to obtain. To settle above issue, in this paper, a novel semi-supervised medical image segmentation method is proposed, in which the adversarial training mechanism and the collaborative consistency learning strategy are introduced into the mean teacher model. With the adversarial training mechanism, the discriminator can generate confidence maps for unlabeled data, such that more reliable supervised information for the student network is exploited. In the process of adversarial training, we further propose a collaborative consistency learning strategy by which the auxiliary discriminator can assist the primary discriminator in achieving supervised information with higher quality. We extensively evaluate our method on three representative yet challenging medical image segmentation tasks: (1) skin lesion segmentation from dermoscopy images in the International Skin Imaging Collaboration (ISIC) 2017 dataset; (2) optic cup and optic disk (OC/O

Who reads Consistency and Adversarial Semi-supervised Learning for Medical Image Segmentation?

Author

Yongqiang Tang; Shilei Wang; Yuxun Qu; Zhihua Cui; Wensheng Zhang

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Medicine (Physical Sciences)