FedRare: Federated Learning with Intra- and Inter-Client Contrast for Effective Rare Disease Classification

by   Nannan Wu, et al.

Federated learning (FL), enabling different medical institutions or clients to train a model collaboratively without data privacy leakage, has drawn great attention in medical imaging communities recently. Though inter-client data heterogeneity has been thoroughly studied, the class imbalance problem due to the existence of rare diseases still is under-explored. In this paper, we propose a novel FL framework FedRare for medical image classification especially on dealing with data heterogeneity with the existence of rare diseases. In FedRare, each client trains a model locally to extract highly-separable latent features for classification via intra-client supervised contrastive learning. Considering the limited data on rare diseases, we build positive sample queues for augmentation (i.e. data re-sampling). The server in FedRare would collect the latent features from clients and automatically select the most reliable latent features as guidance sent back to clients. Then, each client is jointly trained by an inter-client contrastive loss to align its latent features to the federated latent features of full classes. In this way, the parameter/feature variances across clients are effectively minimized, leading to better convergence and performance improvements. Experimental results on the publicly-available dataset for skin lesion diagnosis demonstrate FedRare's superior performance. Under the 10-client federated setting where four clients have no rare disease samples, FedRare achieves an average increase of 9.60 FedAvg and the state-of-the-art approach FedIRM respectively. Considering the board existence of rare diseases in clinical scenarios, we believe FedRare would benefit future FL framework design for medical image classification. The source code of this paper is publicly available at https://github.com/wnn2000/FedRare.


Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

Federated learning (FL) has emerged with increasing popularity to collab...

FCA: Taming Long-tailed Federated Medical Image Classification by Classifier Anchoring

Limited training data and severe class imbalance impose significant chal...

Scale Federated Learning for Label Set Mismatch in Medical Image Classification

Federated learning (FL) has been introduced to the healthcare domain as ...

Federated Model Aggregation via Self-Supervised Priors for Highly Imbalanced Medical Image Classification

In the medical field, federated learning commonly deals with highly imba...

Feature-context driven Federated Meta-Learning for Rare Disease Prediction

Millions of patients suffer from rare diseases around the world. However...

Peer Learning for Skin Lesion Classification

Skin cancer is one of the most deadly cancers worldwide. Yet, it can be ...

Achieving Fairness in Dermatological Disease Diagnosis through Automatic Weight Adjusting Federated Learning and Personalization

Dermatological diseases pose a major threat to the global health, affect...

Please sign up or login with your details

Forgot password? Click here to reset