FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space (2103.06030v1)

Abstract: Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. In this paper, we point out and solve a novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. Our approach transmits the distribution information across clients in a privacy-protecting way through an effective continuous frequency space interpolation mechanism. With the transferred multi-source distributions, we further carefully design a boundary-oriented episodic learning paradigm to expose the local learning to domain distribution shifts and particularly meet the challenges of model generalization in medical image segmentation scenario. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks. The code is available at "https://github.com/liuquande/FedDG-ELCFS".

• The paper introduces an episodic learning framework in continuous frequency space to boost federated domain generalization for medical image segmentation.
• It leverages frequency domain exchange and continuous interpolation to enable multi-source learning without compromising patient privacy.
• Experimental results on retinal fundus and prostate MRI segmentation show improvements in Dice coefficients and Hausdorff distances, enhancing model performance on unseen domains.

Federated Domain Generalization in Medical Image Segmentation: A Review of FedDG Paper

The paper "FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space" introduces a novel approach to address the problem of Federated Domain Generalization (FedDG) within the context of medical image segmentation. The authors present a methodology aiming to enhance the generalizability of models trained using federated learning (FL), particularly when applied to unseen target domains that differ from the distributed source domains used during training.

Key Contributions

The authors identify and tackle the FedDG challenge by proposing a new approach termed Episodic Learning in Continuous Frequency Space (ELCFS). The methodology centers on enabling each FL client in the network to leverage multi-source data distributions despite the inherent constraints of data decentralization, while preserving privacy. This innovation is underpinned by three core ideas:

1. Frequency Space-Based Distribution Information Exchange: By utilizing the amplitude spectrum of images, which encapsulates low-level distribution characteristics in the frequency domain, and exchanging this across clients, the model overcomes the impediments posed by privacy restrictions. This approach ensures that the high-level semantic content, preserved in the phase spectrum, remains confidential.
2. Continuous Frequency Space Interpolation: A mechanism is implemented to interpolate between local and transferred distributions in a continuous manner. This enriches the established multi-domain data scenarios, enhancing the model's capacity to learn domain-invariant features.
3. Boundary-Oriented Episodic Learning: This component specifically addresses the nuances of medical image segmentation. It focuses episodic training efforts on boundary regions of medical images, thereby increasing discriminability and reducing errors often encountered at anatomical boundaries.

Experimental Validation

The efficacy of the proposed approach is validated through comprehensive experiments on two critical medical imaging tasks: retinal fundus image segmentation and prostate MRI segmentation. The results reveal superior performance in Dice coefficients and Hausdorff distances compared to state-of-the-art domain generalization (DG) methods and the baseline federated averaging (FedAvg) algorithm. Notably, the proposed FedDG approach outperformed these techniques in handling unseen domains, as demonstrated in metrics like a 2.02% improvement in Dice and a 2.86 reduction in Hausdorff distance for fundus segmentation.

Implications and Future Directions

The successful application of ELCFS in medical imaging underscores the potential impact of federated domain generalization frameworks on clinical practice. By augmenting the ability of federated models to deal with distribution shifts inherent in diverse medical datasets, FedDG fosters improved generalization without compromising patient privacy. Furthermore, the concepts introduced in this work can be adapted to other modalities and applications beyond medical imaging, where data privacy and heterogeneity pose significant challenges.

These findings encourage further exploration into the scalability of frequency space transformations and episodic learning strategies. Future work could delve into refining these techniques, exploring their applicability across broader medical and non-medical domains, and integrating real-time federated learning systems in practice.

In conclusion, the paper sets a precedent for enhancing federated learning models' generalizability, potentially transforming how data-driven models operate across varied and decentralized environments in healthcare and beyond.