Exemplar Learning for Medical Image Segmentation
- URL: http://arxiv.org/abs/2204.01713v1
- Date: Sun, 3 Apr 2022 00:10:06 GMT
- Title: Exemplar Learning for Medical Image Segmentation
- Authors: Qing En, Yuhong Guo
- Abstract summary: We propose an Exemplar Learning-based Synthesis Net (ELSNet) framework for medical image segmentation.
ELSNet introduces two new modules for image segmentation: an exemplar-guided synthesis module and a pixel-prototype based contrastive embedding module.
We conduct experiments on several organ segmentation datasets and present an in-depth analysis.
- Score: 38.61378161105941
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Medical image annotation typically requires expert knowledge and hence incurs
time-consuming and expensive data annotation costs. To reduce this burden, we
propose a novel learning scenario, Exemplar Learning (EL), to explore automated
learning processes for medical image segmentation from a single annotated image
example. This innovative learning task is particularly suitable for medical
image segmentation, where all categories of organs can be presented in one
single image for annotation all at once. To address this challenging EL task,
we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework for
medical image segmentation that enables innovative exemplar-based data
synthesis, pixel-prototype based contrastive embedding learning, and
pseudo-label based exploitation of the unlabeled data. Specifically, ELSNet
introduces two new modules for image segmentation: an exemplar-guided synthesis
module, which enriches and diversifies the training set by synthesizing
annotated samples from the given exemplar, and a pixel-prototype based
contrastive embedding module, which enhances the discriminative capacity of the
base segmentation model via contrastive self-supervised learning. Moreover, we
deploy a two-stage process for segmentation model training, which exploits the
unlabeled data with predicted pseudo segmentation labels. To evaluate this new
learning framework, we conduct extensive experiments on several organ
segmentation datasets and present an in-depth analysis. The empirical results
show that the proposed exemplar learning framework produces effective
segmentation results.
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