Enhancing X-ray Image Classification through Heterogeneous Federated Learning with Natural Image-Augmented Models.

Abstract

Deep learning-based computer-aided diagnosis (DL-CAD) models have achieved remarkable success in X-ray image analysis. Yet their effectiveness is often constrained by the tight regulations of governing sensitive X-ray data. Federated Learning (FL) offers a promising paradigm by facilitating the collaborative training of DL-CAD models across healthcare institutions without compromising data privacy. Despite this advancement, the limited local X-ray archives and the issue of heterogeneous model architectures bring distinctive challenges. To address these challenges, this work pioneers the utilization of natural images to develop a natural image-augmented heterogeneous FL framework (NatIMG-FL) for X-ray classification. For augmenting local training, NatIMG-FL leverages natural images as auxiliary supervised data to facilitate the alignment of feature distributions between natural and X-ray images. To tackle the model heterogeneity issue, NatIMG-FL introduces a novel dual weights-based fine-grained knowledge transfer method, enabling adaptive knowledge exchange between local and central models. The NatIMG-FL framework provides insights into exploiting natural images as proxy datasets to enhance knowledge transfer in heterogeneous FL for X-ray classification.