Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation   Segmentation of infected areas in chest CT volumes is of great significance
for further diagnosis and treatment of COVID-19 patients. Due to the complex
shapes and varied appearances of lesions, a large number of voxel-level labeled
samples are generally required to train a lesion segmentation network, which is
a main bottleneck for developing deep learning based medical image segmentation
algorithms. In this paper, we propose a weakly-supervised lesion segmentation
framework by embedding the Generative Adversarial training process into the
Segmentation Network, which is called GASNet. GASNet is optimized to segment
the lesion areas of a COVID-19 CT by the segmenter, and to replace the abnormal
appearance with a generated normal appearance by the generator, so that the
restored CT volumes are indistinguishable from healthy CT volumes by the
discriminator. GASNet is supervised by chest CT volumes of many healthy and
COVID-19 subjects without voxel-level annotations. Experiments on three public
databases show that when using as few as one voxel-level labeled sample, the
performance of GASNet is comparable to fully-supervised segmentation algorithms
trained on dozens of voxel-level labeled samples.

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The categories of this paper are: q-alg
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: funct-an
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: nlin