Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging (2205.08576v2)

Abstract: The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing. Federated learning (FL) is a promising solution that enables privacy-preserving collaborative learning among different institutions, but it generally suffers from performance deterioration due to heterogeneous data distributions and a lack of quality labeled data. In this paper, we present a robust and label-efficient self-supervised FL framework for medical image analysis. Our method introduces a novel Transformer-based self-supervised pre-training paradigm that pre-trains models directly on decentralized target task datasets using masked image modeling, to facilitate more robust representation learning on heterogeneous data and effective knowledge transfer to downstream models. Extensive empirical results on simulated and real-world medical imaging non-IID federated datasets show that masked image modeling with Transformers significantly improves the robustness of models against various degrees of data heterogeneity. Notably, under severe data heterogeneity, our method, without relying on any additional pre-training data, achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training. In addition, we show that our federated self-supervised pre-training methods yield models that generalize better to out-of-distribution data and perform more effectively when fine-tuning with limited labeled data, compared to existing FL algorithms. The code is available at https://github.com/rui-yan/SSL-FL.

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging

In the domain of medical imaging, where the collection of large-scale datasets across various institutions presents both opportunities and challenges, federated learning (FL) emerges as a pivotal solution. The paper "Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging" addresses the pressing issues of data heterogeneity and label deficiency in decentralized machine learning scenarios. The authors introduce a novel self-supervised FL framework aimed at improving model performance in the face of heterogeneous data distributions without the necessity for extensive labeled datasets.

The research proposes an innovative approach combining Vision Transformers (ViTs) and masked image modeling to overcome the limitations typically encountered in FL, where data is often non-IID. The framework leverages self-supervised pre-training directly on decentralized target task datasets, facilitating robust representation learning and effective knowledge transfer to downstream tasks. Specifically, the method incorporates masked autoencoder strategies, such as those seen in BEiT and MAE models, to pre-train models on unlabeled data, which complements traditional supervised learning algorithms.

Empirical evaluations across various medical datasets—retinal, dermatological, and chest X-rays—demonstrate significant improvements in test accuracy under severe data heterogeneity. The proposed method advances test accuracy compared to established supervised baselines pre-trained on ImageNet, showing improvements of 5.06%, 1.53%, and 4.58% in retinal, dermatology, and chest X-ray classification tasks respectively, under challenging non-IID conditions. These results highlight the effectiveness of self-supervised learning via FL in adapting to distributed and diverse data sources prevalent in medical domains.

Theoretical implications of this paper suggest a shift towards integrating self-supervised strategies in federated learning frameworks, especially when applied to medical imaging where privacy concerns limit data accessibility among institutions. Practically, this research provides a basis for developing more intelligent systems capable of learning robust representations from decentralized data, potentially lowering the barrier to deploying high-performing models in healthcare settings with limited data annotation capabilities.

Future work may explore more comprehensive applications of the framework across diverse medical domains, investigating its adaptability to other forms of medical data such as electronic health records or sensor data. Additionally, further studies on fine-tuning strategies or model initialization techniques could enhance the self-supervised learning paradigm even further, paving the way for scalable, privacy-aware AI solutions in clinical practice.

In conclusion, this paper makes a significant contribution to federated learning, offering a viable pathway to improve model efficacy amidst data heterogeneity and annotation challenges, thus promising advancements in AI-driven medical intelligence systems.