Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-stained Segmentation from Renal Biopsy Images Using Multi-level
  Adversarial Learning   Segmentation from renal pathological images is a key step in automatic
analyzing the renal histological characteristics. However, the performance of
models varies significantly in different types of stained datasets due to the
appearance variations. In this paper, we design a robust and flexible model for
cross-stained segmentation. It is a novel multi-level deep adversarial network
architecture that consists of three sub-networks: (i) a segmentation network;
(ii) a pair of multi-level mirrored discriminators for guiding the segmentation
network to extract domain-invariant features; (iii) a shape discriminator that
is utilized to further identify the output of the segmentation network and the
ground truth. Experimental results on glomeruli segmentation from renal biopsy
images indicate that our network is able to improve segmentation performance on
target type of stained images and use unlabeled data to achieve similar
accuracy to labeled data. In addition, this method can be easily applied to
other tasks.

3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph