Universal Medical Imaging Model for Domain Generalization with Data Privacy
This addresses the problem of limited labeled data and privacy concerns in medical imaging for healthcare applications, though it is incremental as it builds on existing federated learning and domain generalization methods.
The paper tackles domain generalization in medical imaging by proposing a federated learning approach that transfers knowledge from local models to a global model without accessing private datasets, achieving statistically significant improvement over a state-of-the-art baseline across eight diverse medical imaging datasets.
Achieving domain generalization in medical imaging poses a significant challenge, primarily due to the limited availability of publicly labeled datasets in this domain. This limitation arises from concerns related to data privacy and the necessity for medical expertise to accurately label the data. In this paper, we propose a federated learning approach to transfer knowledge from multiple local models to a global model, eliminating the need for direct access to the local datasets used to train each model. The primary objective is to train a global model capable of performing a wide variety of medical imaging tasks. This is done while ensuring the confidentiality of the private datasets utilized during the training of these models. To validate the effectiveness of our approach, extensive experiments were conducted on eight datasets, each corresponding to a different medical imaging application. The client's data distribution in our experiments varies significantly as they originate from diverse domains. Despite this variation, we demonstrate a statistically significant improvement over a state-of-the-art baseline utilizing masked image modeling over a diverse pre-training dataset that spans different body parts and scanning types. This improvement is achieved by curating information learned from clients without accessing any labeled dataset on the server.