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

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Computer Science

Background:

• Deep learning-based computer-aided diagnosis (DL-CAD) models show promise in X-ray analysis but face data privacy constraints.
• Federated Learning (FL) enables collaborative model training across institutions without sharing sensitive X-ray data.
• Challenges in FL for X-ray classification include limited local data and heterogeneous model architectures.

Purpose of the Study:

• To develop a novel natural image-augmented heterogeneous FL framework (NatIMG-FL) for X-ray classification.
• To leverage natural images as auxiliary data to align feature distributions and enhance local training.
• To address model heterogeneity using a dual weights-based fine-grained knowledge transfer method.

Main Methods:

• Developed the NatIMG-FL framework integrating natural images into heterogeneous FL for X-ray classification.
• Utilized natural images as auxiliary supervised data to bridge feature distribution gaps.
• Implemented a dual weights-based fine-grained knowledge transfer mechanism for adaptive model exchange.

Main Results:

• The NatIMG-FL framework effectively uses natural images to augment limited X-ray data for improved classification.
• The dual weights method facilitates adaptive knowledge transfer, mitigating challenges from heterogeneous model architectures.
• Demonstrated enhanced performance in federated X-ray classification by aligning feature spaces.

Conclusions:

• Natural images can serve as effective proxy datasets in FL for medical imaging tasks.
• The proposed NatIMG-FL framework offers a viable solution for privacy-preserving, heterogeneous FL in X-ray diagnostics.
• This approach advances DL-CAD by overcoming data limitations and architectural diversity in collaborative learning.