Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Deep Learning Classification of Unstructured Pathology
  Reports to Automate ICD-O Morphology Grading   Timely cancer reporting data are required in order to understand the impact
of cancer, inform public health resource planning and implement cancer policy
especially in Sub Saharan Africa where the reporting lag is behind world
averages. Unstructured pathology reports, which contain tumor specific data,
are the main source of information collected by cancer registries. Due to
manual processing and labelling of pathology reports using the International
Classification of Disease for oncology (ICD-O) codes, by human coders employed
by cancer registries, has led to a considerable lag in cancer reporting. We
present a hierarchical deep learning classification method that employs
convolutional neural network models to automate the classification of 1813
anonymized breast cancer pathology reports with applicable ICD-O morphology
codes across 9 classes. We demonstrate that the hierarchical deep learning
classification method improves on performance in comparison to a flat
multiclass CNN model for ICD-O morphology classification of the same reports.

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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: atom-ph