Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention-Based Deep Neural Networks for Detection of Cancerous and
  Precancerous Esophagus Tissue on Histopathological Slides   Deep learning-based methods, such as the sliding window approach for
cropped-image classification and heuristic aggregation for whole-slide
inference, for analyzing histological patterns in high-resolution microscopy
images have shown promising results. These approaches, however, require a
laborious annotation process and are fragmented. This diagnostic study
collected deidentified high-resolution histological images (N = 379) for
training a new model composed of a convolutional neural network and a
grid-based attention network, trainable without region-of-interest annotations.
Histological images of patients who underwent endoscopic esophagus and
gastroesophageal junction mucosal biopsy between January 1, 2016, and December
31, 2018, at Dartmouth-Hitchcock Medical Center (Lebanon, New Hampshire) were
collected. The method achieved a mean accuracy of 0.83 in classifying 123 test
images. These results were comparable with or better than the performance from
the current state-of-the-art sliding window approach, which was trained with
regions of interest. Results of this study suggest that the proposed
attention-based deep neural network framework for Barrett esophagus and
esophageal adenocarcinoma detection is important because it is based solely on
tissue-level annotations, unlike existing methods that are based on regions of
interest. This new model is expected to open avenues for applying deep learning
to digital pathology.

32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th