  Recent studies have shown promising results in using Deep Learning to detect
malignancy in whole slide imaging. However, they were limited to just
predicting positive or negative finding for a specific neoplasm. We attempted
to use Deep Learning with a convolutional neural network algorithm to build a
lymphoma diagnostic model for four diagnostic categories: benign lymph node,
diffuse large B cell lymphoma, Burkitt lymphoma, and small lymphocytic
lymphoma. Our software was written in Python language. We obtained digital
whole slide images of Hematoxylin and Eosin stained slides of 128 cases
including 32 cases for each diagnostic category. Four sets of 5 representative
images, 40x40 pixels in dimension, were taken for each case. A total of 2,560
images were obtained from which 1,856 were used for training, 464 for
validation and 240 for testing. For each test set of 5 images, the predicted
diagnosis was combined from prediction of 5 images. The test results showed
excellent diagnostic accuracy at 95% for image-by-image prediction and at 10%
for set-by-set prediction. This preliminary study provided a proof of concept
for incorporating automated lymphoma diagnostic screen into future pathology
workflow to augment the pathologists' productivity.
