Semi-Supervised Semantic Segmentation of Vessel Images using Leaking
Perturbations
- URL: http://arxiv.org/abs/2110.11998v1
- Date: Fri, 22 Oct 2021 18:25:08 GMT
- Title: Semi-Supervised Semantic Segmentation of Vessel Images using Leaking
Perturbations
- Authors: Jinyong Hou, Xuejie Ding, Jeremiah D. Deng
- Abstract summary: Leaking GAN is a GAN-based semi-supervised architecture for retina vessel semantic segmentation.
Our key idea is to pollute the discriminator by leaking information from the generator.
This leads to more moderate generations that benefit the training of GAN.
- Score: 1.5791732557395552
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Semantic segmentation based on deep learning methods can attain appealing
accuracy provided large amounts of annotated samples. However, it remains a
challenging task when only limited labelled data are available, which is
especially common in medical imaging. In this paper, we propose to use Leaking
GAN, a GAN-based semi-supervised architecture for retina vessel semantic
segmentation. Our key idea is to pollute the discriminator by leaking
information from the generator. This leads to more moderate generations that
benefit the training of GAN. As a result, the unlabelled examples can be better
utilized to boost the learning of the discriminator, which eventually leads to
stronger classification performance. In addition, to overcome the variations in
medical images, the mean-teacher mechanism is utilized as an auxiliary
regularization of the discriminator. Further, we modify the focal loss to fit
it as the consistency objective for mean-teacher regularizer. Extensive
experiments demonstrate that the Leaking GAN framework achieves competitive
performance compared to the state-of-the-art methods when evaluated on
benchmark datasets including DRIVE, STARE and CHASE\_DB1, using as few as 8
labelled images in the semi-supervised setting. It also outperforms existing
algorithms on cross-domain segmentation tasks.
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