Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Classifying Mammographic Breast Density by Residual Learning   Mammographic breast density, a parameter used to describe the proportion of
breast tissue fibrosis, is widely adopted as an evaluation characteristic of
the likelihood of breast cancer incidence. In this study, we present a
radiomics approach based on residual learning for the classification of
mammographic breast densities. Our method possesses several encouraging
properties such as being almost fully automatic, possessing big model capacity
and flexibility. It can obtain outstanding classification results without the
necessity of result compensation using mammographs taken from different views.
The proposed method was instantiated with the INbreast dataset and
classification accuracies of 92.6% and 96.8% were obtained for the four BI-RADS
(Breast Imaging and Reporting Data System) category task and the two BI-RADS
category task,respectively. The superior performances achieved compared to the
existing state-of-the-art methods along with its encouraging properties
indicate that our method has a great potential to be applied as a
computer-aided diagnosis tool.

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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex