Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in
  Vessel Networks   Retinal vessel segmentation is of great interest for diagnosis of retinal
vascular diseases. To further improve the performance of vessel segmentation,
we propose IterNet, a new model based on UNet, with the ability to find
obscured details of the vessel from the segmented vessel image itself, rather
than the raw input image. IterNet consists of multiple iterations of a
mini-UNet, which can be 4$\times$ deeper than the common UNet. IterNet also
adopts the weight-sharing and skip-connection features to facilitate training;
therefore, even with such a large architecture, IterNet can still learn from
merely 10$\sim$20 labeled images, without pre-training or any prior knowledge.
IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream
datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are
the best scores in the literature. The source code is available.

36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: dg-ga