Lung Nodule Segmentation Using Federated Active Learning

Abstract

Lung nodule segmentation on computed tomography (CT) is at the same time one of the most common and laborious tasks in oncological radiology. Fortunately, artificial intelligence agents have been showing promising results in streamlining the process. We study some of the challenges of training an AI model for lung nodule segmentation, including the degradation of performance due to distribution shift, privacy concerns and limited bandwidth for cloud data transmission. The article explores different federated learning strategies, over a pool of 1506 CT studies collected from four hospitals. The results show that federated learning models reach near standard classical training DICE score performance (i.e., 87.24% vs. 88.96%), and even surpass it in a privacy-centered context (i.e., 87.24% vs. 84.78%). Additionally, active learning was proven to increase the new model's DICE score by 1.76% over the random sampling strategy. The article adds to the growing body of research exploring the use of federated learning in healthcare and demonstrates its potential for improving lung nodule segmentation on CT.