FedDG-ELCFS
Federated Learning Framework
A framework for federated learning on medical image segmentation using continuous frequency space interpolation.
[CVPR'21] FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space
240 stars
8 watching
34 forks
Language: Python
last commit: over 3 years ago Related projects:
| Repository | Description | Stars |
|---|---|---|
 jcwang123/fedlc | An implementation of personalized federated medical image segmentation via local calibration for medical image analysis | 45 |
 fyu/dilation | This project provides a deep learning framework implementing dilated convolutions for semantic image segmentation | 781 |
 tfzhou/fedfa | An ICLR 2023 paper implementation in PyTorch of Federated Feature Augmentation for federated learning with data augmentation and medical image analysis. | 57 |
 gaoliang13/feddc | Federated learning algorithm that adapts to non-IID data by decoupling and correcting for local drift | 79 |
 mediabrain-sjtu/feddg-ga | This project presents a method for federated domain generalization with adjustment, allowing multiple models to learn from each other across different domains. | 43 |
 tobypde/frrn | A software framework for training and evaluating full-resolution residual networks for semantic image segmentation tasks | 280 |
 jhoon-oh/fedbabu | An implementation of federated learning for image classification tasks | 51 |
 yinboc/liif | This project presents an approach to learning continuous image representation using a local implicit function. | 1,271 |
| mediabrain-sjtu/pfedgraph | This project enables personalized federated learning with inferred collaboration graphs to improve the performance of machine learning models on non-IID (non-independent and identically distributed) datasets. | 26 |
 jiahuadong/fiss | Implementations of federated incremental semantic segmentation in PyTorch. | 33 |
 lx10077/fedavgpy | The purpose of this project is to investigate the convergence of a federated learning algorithm on non-IID (non-identically and independently distributed) data. | 250 |
 guopengf/fl-mrcm | Improves deep learning-based magnetic resonance image reconstruction using federated learning and multi-institutional collaboration | 46 |
 med-air/fedbn | An approach to federated learning that addresses feature shift non-iid by normalizing local batch features before averaging models. | 231 |
 chunmeifeng/fedpr | An algorithm for learning federated visual prompts in null space to improve MRI reconstruction performance on limited local data and reduced communication costs | 42 |
 hyhmia/distrans | Improves federated learning models by addressing data heterogeneity through distributional transformation | 5 |