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

Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning