Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Explainable Medical Image Segmentation via Generative Adversarial
  Networks and Layer-wise Relevance Propagation   This paper contributes to automating medical image segmentation by proposing
generative adversarial network-based models to segment both polyps and
instruments in endoscopy images. A major contribution of this work is to
provide explanations for the predictions using a layer-wise relevance
propagation approach designating which input image pixels are relevant to the
predictions and to what extent. On the polyp segmentation task, the models
achieved 0.84 of accuracy and 0.46 on Jaccard index. On the instrument
segmentation task, the models achieved 0.96 of accuracy and 0.70 on Jaccard
index. The code is available at https://github.com/Awadelrahman/MedAI.

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The categories of this paper are: dg-ga
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The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
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The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat