FedStenoNet: tackling domain shift in x-ray coronary angiography through a personalized federated detection framework.

Abstract

Background and objective: The automatic identification of coronary stenosis in x-ray coronary angiography (XCA) is hindered by the variability in imaging protocols and patient characteristics across different hospitals, leading to significant domain shifts. These challenges impact the ability of algorithms to generalize effectively across diverse clinical environments. This study aims to address these issues by proposing FedStenoNet, a personalized federated learning (PFL) framework tailored for enhanced stenosis detection.

Methods: In place of a single global model, FedStenoNet shares only backbone weights across clients and customizes the model to each client's specific data distribution. The framework also incorporates histogram matching to tackle inter-dataset variability and a novel test-time adaptation algorithm to mitigate intra-dataset variability.

Results: Evaluation of FedStenoNet across three non-identical and independently distributed datasets (one released with this study) demonstrated an average F1-score of 50.82%. FedStenoNet shows promising diagnostic accuracy in a challenging domain, where achieving high performance has proven difficult.

Conclusions: By managing domain shifts via FedStenoNet, this study sets a promising direction for future research, further supported by the release of one XCA dataset.