Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Self-Loop Uncertainty: A Novel Pseudo-Label for Semi-Supervised Medical
  Image Segmentation   Witnessing the success of deep learning neural networks in natural image
processing, an increasing number of studies have been proposed to develop
deep-learning-based frameworks for medical image segmentation. However, since
the pixel-wise annotation of medical images is laborious and expensive, the
amount of annotated data is usually deficient to well-train a neural network.
In this paper, we propose a semi-supervised approach to train neural networks
with limited labeled data and a large quantity of unlabeled images for medical
image segmentation. A novel pseudo-label (namely self-loop uncertainty),
generated by recurrently optimizing the neural network with a self-supervised
task, is adopted as the ground-truth for the unlabeled images to augment the
training set and boost the segmentation accuracy. The proposed self-loop
uncertainty can be seen as an approximation of the uncertainty estimation
yielded by ensembling multiple models with a significant reduction of inference
time. Experimental results on two publicly available datasets demonstrate the
effectiveness of our semi-supervied approach.

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The categories of this paper are: cond-mat
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The categories of this paper are: econ
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: math
22
The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: gr-qc
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The categories of this paper are: astro-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph