Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gastric Cancer Detection from X-ray Images Using Effective Data
  Augmentation and Hard Boundary Box Training   X-ray examination is suitable for screening of gastric cancer. Compared to
endoscopy, which can only be performed by doctors, X-ray imaging can also be
performed by radiographers, and thus, can treat more patients. However, the
diagnostic accuracy of gastric radiographs is as low as 85%. To address this
problem, highly accurate and quantitative automated diagnosis using machine
learning needs to be performed. This paper proposes a diagnostic support method
for detecting gastric cancer sites from X-ray images with high accuracy. The
two new technical proposal of the method are (1) stochastic functional gastric
image augmentation (sfGAIA), and (2) hard boundary box training (HBBT). The
former is a probabilistic enhancement of gastric folds in X-ray images based on
medical knowledge, whereas the latter is a recursive retraining technique to
reduce false positives. We use 4,724 gastric radiographs of 145 patients in
clinical practice and evaluate the cancer detection performance of the method
in a patient-based five-group cross-validation. The proposed sfGAIA and HBBT
significantly enhance the performance of the EfficientDet-D7 network by 5.9% in
terms of the F1-score, and our screening method reaches a practical screening
capability for gastric cancer (F1: 57.8%, recall: 90.2%, precision: 42.5%).

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The categories of this paper are: hep-ex
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: comp-gas
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The categories of this paper are: econ
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The categories of this paper are: dg-ga