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Recent Methodological Advances in Federated Learning for Healthcare

In this paper, we perform a detailed review of new federated learning methodologies for addressing challenges with healthcare data. We also give detailed recommendations to help improve the quality of the methodology development.

Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data

In this paper, we propose a novel model architecture for domain generalisation, namely a disentangled autoencoder (Dis-AE).
Dis-AE provides a scalable solution for future research on large collaborative medical datasets suffering from domain shift on many domains. This may be particularly useful in multi-centre studies and trials using repeated measurements.

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