Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adversarial Domain Adaptation for Classification of Prostate
  Histopathology Whole-Slide Images   Automatic and accurate Gleason grading of histopathology tissue slides is
crucial for prostate cancer diagnosis, treatment, and prognosis. Usually,
histopathology tissue slides from different institutions show heterogeneous
appearances because of different tissue preparation and staining procedures,
thus the predictable model learned from one domain may not be applicable to a
new domain directly. Here we propose to adopt unsupervised domain adaptation to
transfer the discriminative knowledge obtained from the source domain to the
target domain without requiring labeling of images at the target domain. The
adaptation is achieved through adversarial training to find an invariant
feature space along with the proposed Siamese architecture on the target domain
to add a regularization that is appropriate for the whole-slide images. We
validate the method on two prostate cancer datasets and obtain significant
classification improvement of Gleason scores as compared with the baseline
models.

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The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
9
The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-lat
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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: q-fin