Improving Follicular Lymphoma Identification using the Class of Interest for Transfer Learning

Abstract

Follicular Lymphoma (FL) is a type of lymphoma that grows silently and is usually diagnosed in its later stages. To increase the patients' survival rates, FL requires a fast diagnosis. While, traditionally, the diagnosis is performed by visual inspection of Whole Slide Images (WSI), recent advances in deep learning techniques provide an opportunity to automate this process. The main challenge, however, is that WSI images often exhibit large variations across different operating environments, hereinafter referred to as sites. As such, deep learning models usually require retraining using labeled data from each new site. This is, however, not feasible since the labelling process requires pathologists to visually inspect and label each sample. In this paper, we propose a deep learning model that uses transfer learning with fine-tuning to improve the identification of Follicular Lymphoma on images from new sites that are different from those used during training. Our results show that the proposed approach improves the prediction accuracy with 12% to 52% compared to the initial prediction of the model for images from a new site in the target environment.