Triple-View Feature Learning for Medical Image Segmentation
- URL: http://arxiv.org/abs/2208.06303v1
- Date: Fri, 12 Aug 2022 14:41:40 GMT
- Title: Triple-View Feature Learning for Medical Image Segmentation
- Authors: Ziyang Wang, Irina Voiculescu
- Abstract summary: TriSegNet is a semi-supervised semantic segmentation framework.
It uses triple-view feature learning on a limited amount of labelled data and a large amount of unlabeled data.
- Score: 9.992387025633805
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit
promising performance in medical image segmentation, but come with a high
labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation
framework. It uses triple-view feature learning on a limited amount of labelled
data and a large amount of unlabeled data. The triple-view architecture
consists of three pixel-level classifiers and a low-level shared-weight
learning module. The model is first initialized with labelled data. Label
processing, including data perturbation, confidence label voting and
unconfident label detection for annotation, enables the model to train on
labelled and unlabeled data simultaneously. The confidence of each model gets
improved through the other two views of the feature learning. This process is
repeated until each model reaches the same confidence level as its
counterparts. This strategy enables triple-view learning of generic medical
image datasets. Bespoke overlap-based and boundary-based loss functions are
tailored to the different stages of the training. The segmentation results are
evaluated on four publicly available benchmark datasets including Ultrasound,
CT, MRI, and Histology images. Repeated experiments demonstrate the
effectiveness of the proposed network compared against other semi-supervised
algorithms, across a large set of evaluation measures.
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