Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning

TL;DR

This study demonstrates a deep learning model using convolutional neural networks to classify four lymphoma diagnostic categories from digital pathology images with high accuracy, aiming to enhance pathology workflows.

Contribution

It introduces a novel CNN-based approach for multi-class lymphoma diagnosis from whole slide images, expanding beyond binary predictions.

Findings

Achieved 95% accuracy in image-by-image diagnosis

Successfully classified four lymphoma categories

Provided proof of concept for automated lymphoma screening

Abstract

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.