Accurate diagnosis of lymphoma on whole-slide histopathology images using deep learning

TLDR

Histopathological diagnosis of lymphomas is challenging, requiring expert or centralised review and being highly dependent on tissue processing. We developed a deep‑learning framework with certainty estimation for H&E‑stained slides, focusing on follicular lymphoma diagnosis. Bayesian neural networks were trained, validated, and tested on whole‑slide images of lymph nodes with FL or follicular hyperplasia. The BNN achieved an AUC of 0.99, accurately diagnosed FL, detected unfamiliar data via uncertainty estimation, and underscored the importance of careful slide pre‑processing for universal diagnostic tools.

Abstract

Histopathological diagnosis of lymphomas represents a challenge requiring either expertise or centralised review, and greatly depends on the technical process of tissue sections. Hence, we developed an innovative deep-learning framework, empowered with a certainty estimation level, designed for haematoxylin and eosin-stained slides analysis, with special focus on follicular lymphoma (FL) diagnosis. Whole-slide images of lymph nodes affected by FL or follicular hyperplasia were used for training, validating, and finally testing Bayesian neural networks (BNN). These BNN provide a diagnostic prediction coupled with an effective certainty estimation, and generate accurate diagnosis with an area under the curve reaching 0.99. Through its uncertainty estimation, our network is also able to detect unfamiliar data such as other small B cell lymphomas or technically heterogeneous cases from external centres. We demonstrate that machine-learning techniques are sensitive to the pre-processing of histopathology slides and require appropriate training to build universal tools to aid diagnosis.