Distributed Contrastive Learning for Medical Image Segmentation

by   Yawen Wu, et al.

Supervised deep learning needs a large amount of labeled data to achieve high performance. However, in medical imaging analysis, each site may only have a limited amount of data and labels, which makes learning ineffective. Federated learning (FL) can learn a shared model from decentralized data. But traditional FL requires fully-labeled data for training, which is very expensive to obtain. Self-supervised contrastive learning (CL) can learn from unlabeled data for pre-training, followed by fine-tuning with limited annotations. However, when adopting CL in FL, the limited data diversity on each site makes federated contrastive learning (FCL) ineffective. In this work, we propose two federated self-supervised learning frameworks for volumetric medical image segmentation with limited annotations. The first one features high accuracy and fits high-performance servers with high-speed connections. The second one features lower communication costs, suitable for mobile devices. In the first framework, features are exchanged during FCL to provide diverse contrastive data to each site for effective local CL while keeping raw data private. Global structural matching aligns local and remote features for a unified feature space among different sites. In the second framework, to reduce the communication cost for feature exchanging, we propose an optimized method FCLOpt that does not rely on negative samples. To reduce the communications of model download, we propose the predictive target network update (PTNU) that predicts the parameters of the target network. Based on PTNU, we propose the distance prediction (DP) to remove most of the uploads of the target network. Experiments on a cardiac MRI dataset show the proposed two frameworks substantially improve the segmentation and generalization performance compared with state-of-the-art techniques.


page 1

page 4

page 12


Federated Contrastive Learning for Volumetric Medical Image Segmentation

Supervised deep learning needs a large amount of labeled data to achieve...

Federated Alternate Training (FAT): Leveraging Unannotated Data Silos in Federated Segmentation for Medical Imaging

Federated Learning (FL) aims to train a machine learning (ML) model in a...

Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis

In dermatological disease diagnosis, the private data collected by mobil...

Personalizing Federated Medical Image Segmentation via Local Calibration

Medical image segmentation under federated learning (FL) is a promising ...

Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning

Deep learning models have been deployed in an increasing number of edge ...

Federated Contrastive Learning for Decentralized Unlabeled Medical Images

A label-efficient paradigm in computer vision is based on self-supervise...

An Evaluation of Non-Contrastive Self-Supervised Learning for Federated Medical Image Analysis

Privacy and annotation bottlenecks are two major issues that profoundly ...

Please sign up or login with your details

Forgot password? Click here to reset