Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Boosting Connectivity in Retinal Vessel Segmentation via a Recursive
  Semantics-Guided Network   Many deep learning based methods have been proposed for retinal vessel
segmentation, however few of them focus on the connectivity of segmented
vessels, which is quite important for a practical computer-aided diagnosis
system on retinal images. In this paper, we propose an efficient network to
address this problem. A U-shape network is enhanced by introducing a
semantics-guided module, which integrates the enriched semantics information to
shallow layers for guiding the network to explore more powerful features.
Besides, a recursive refinement iteratively applies the same network over the
previous segmentation results for progressively boosting the performance while
increasing no extra network parameters. The carefully designed recursive
semantics-guided network has been extensively evaluated on several public
datasets. Experimental results have shown the efficiency of the proposed
method.

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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-th
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The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: q-bio
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The categories of this paper are: q-alg
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th