Federated Learning with Global Model Hint for Medical Image Object Detection.
TL;DR
This work proposes FedMHDet: Model Hint Federated Learning Detection Model, a novel federated learning detection framework that leverages multi-scale feature consistency as a global model hint to guide client models, thus mitigating the feature drift problem.
Abstract
Building an ideal medical image object detection model often requires sufficient training data, which can be challenging to obtain in practical scenarios. Manual annotation is labor-intensive, and sharing datasets may raise data privacy concerns. Although federated learning can partially address these issues, we find an amplified feature drift problem when it is directly applied to medical image object detection. Motivated by the observation that the global model's parameters tend to align more closely with those of the oracle model than with those of the client models, we propose FedMHDet: Model Hint Federated Learning Detection Model, a novel federated learning detection framework. During the training phase, FedMHDet leverages multi-scale feature consistency as a global model hint to guide client models, thus mitigating the feature drift problem. Extensive experiments on pulmonary lesion and brain tumor detection tasks show that FedMHDet achieves favorable overall performance. Compared to the strongest baseline under each corresponding metric, it improves average AP by 1.05 and 0.19, and average sensitivity by 1.10 and 0.43 on the two tasks, respectively. We also provide in-depth analyses to support the practical use of our method. The code is available at https://github.com/bbamai/FedMHDet.