This repository is the official implementation of Federated Hybrid-Supervised Learning for Universal Medical Image Segmentation.
FL faces difficulties with heterogeneous data and limited annotations in medical image segmentation. Motivated by this discovery, this study proposes a hybrid-supervised federated learning method that explores various types of annotations in medical imaging.
To focus on the weak-supervised and unsupervised scenarios in hybrid-supervised, firstly, a federated Gaussian enhancement module was proposed for heterogeneous sparse annotations. The feature extraction module combines the features of multiple weakly-supervised sites and establishes the correlation of similar pixels, thus making up for the deficiency of scarce annotations and the insufficient feature extraction capability of a single machine. Then, a two-stage broadcast mechanism based on supervision sparsity was proposed to alleviate optimization deviation in local models.
To install requirements:
conda env create -f fedslag.yaml
To train the federated model, run:
python train.py
To evaluate model on breast task, run:
python test_breast.py
A template README.md for code accompanying a Machine Learning paper: README