  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.
