FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

The official implementation of the paper: FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation

Datasets

Download the Datasets

Fundus	OCTA	Endoscopy	Prostate MRI
Download	Download	Download	Download

Details of Training set

The detailed information of datasets can be found in paper

Visualization Results

Requirements

Some important required packages are lised below:

• Pytorch 1.10.2
• cudatoolkit 11.3.1
• efficientnet-pytorch 0.7.1
• tensorboardx 2.5.1
• medpy 0.4.0
• scikit-image 0.19.3
• simpleitk 2.1.1.2
• flwr 1.0.0
• Python >= 3.9

Usage

1. Clone this project

git clone https://github.com/llmir/FedLPPA.git
cd FedLPPA/code_v4

2. Create a conda environment

conda env create -n fed39v2 -f fedlppa.yaml
conda activate fed39v2

3. Data Preparation

You can download the datasets with different formats of sparsely-supervised annotations to the dir 'FedLPPA/data' in the form of '.h5'.

The automated scripts for generating sparsely-supervised annotations will be included in subsequent updates.

4. Train the model

We first disclose the FedAvg and FedLPPA in 'train.sh'. The other comparative methods' scripts will be included in subsequent updates.

##Server
python flower_pCE_2D_v4_FedLPPA.py --root_path ../data/FAZ_h5 --num_classes 2 --in_chns 1 --img_class faz --exp faz/FedLPPA --model unet_univ5 --max_iterations 30000 --iters 5 --eval_iters 5 --tsne_iters 200 --batch_size 12 --base_lr 0.01 --amp 0 --server_address 127.0.0.1:8091 --strategy FedUniV2.1 --min_num_clients 5 --img_size 256 --alpha 0.1 --beta 0.5 --prompt universal --attention dual --dual_init aggregated --label_prompt 1 --role server --client client_all --sup_type mask --gpu 0

##Site A
python flower_pCE_2D_v4_FedLPPA.py --root_path ../data/FAZ_h5 --num_classes 2 --in_chns 1 --img_class faz --exp faz/FedLPPA --model unet_univ5 --max_iterations 30000 --iters 5 --eval_iters 5 --tsne_iters 200 --batch_size 12 --base_lr 0.01 --amp 0 --server_address 127.0.0.1:8091 --strategy FedUniV2.1 --min_num_clients 5 --img_size 256 --alpha 0.1 --beta 0.5 --prompt universal --attention dual --dual_init aggregated --label_prompt 1 --role client --cid 0 --client client1 --sup_type scribble_noisy --gpu 1

##Site B
python flower_pCE_2D_v4_FedLPPA.py --root_path ../data/FAZ_h5 --num_classes 2 --in_chns 1 --img_class faz --exp faz/FedLPPA --model unet_univ5 --max_iterations 30000 --iters 5 --eval_iters 5 --tsne_iters 200 --batch_size 12 --base_lr 0.01 --amp 0 --server_address 127.0.0.1:8091 --strategy FedUniV2.1 --min_num_clients 5 --img_size 256 --alpha 0.1 --beta 0.5 --prompt universal --attention dual --dual_init aggregated --label_prompt 1 --role client --cid 1 --client client2 --sup_type keypoint --gpu 2

##Site C
python flower_pCE_2D_v4_FedLPPA.py --root_path ../data/FAZ_h5 --num_classes 2 --in_chns 1 --img_class faz --exp faz/FedLPPA --model unet_univ5 --max_iterations 30000 --iters 5 --eval_iters 5 --tsne_iters 200 --batch_size 12 --base_lr 0.01 --amp 0 --server_address 127.0.0.1:8091 --strategy FedUniV2.1 --min_num_clients 5 --img_size 256 --alpha 0.1 --beta 0.5 --prompt universal --attention dual --dual_init aggregated --label_prompt 1 --role client --cid 2 --client client3 --sup_type block --gpu 3

##Site D
python flower_pCE_2D_v4_FedLPPA.py --root_path ../data/FAZ_h5 --num_classes 2 --in_chns 1 --img_class faz --exp faz/FedLPPA --model unet_univ5 --max_iterations 30000 --iters 5 --eval_iters 5 --tsne_iters 200 --batch_size 12 --base_lr 0.01 --amp 0 --server_address 127.0.0.1:8091 --strategy FedUniV2.1 --min_num_clients 5 --img_size 256 --alpha 0.1 --beta 0.5 --prompt universal --attention dual --dual_init aggregated --label_prompt 1 --role client --cid 3 --client client4 --sup_type box --gpu 4

##Site E
python flower_pCE_2D_v4_FedLPPA.py --root_path ../data/FAZ_h5 --num_classes 2 --in_chns 1 --img_class faz --exp faz/FedLPPA --model unet_univ5 --max_iterations 30000 --iters 5 --eval_iters 5 --tsne_iters 200 --batch_size 12 --base_lr 0.01 --amp 0 --server_address 127.0.0.1:8091 --strategy FedUniV2.1 --min_num_clients 5 --img_size 256 --alpha 0.1 --beta 0.5 --prompt universal --attention dual --dual_init aggregated --label_prompt 1 --role client --cid 4 --client client5 --sup_type scribble --gpu 5

• root_path: The dataset root path.
• num_classes: The segmentation classes.
• batch_size: 12.
• image_size: Default value is 256.
• exp: save_path of models and 'log' file.
• server_address: Server communication port. If you train the server model and client models in one server, please set it to the similar format '127.0.0.1:8091' ( note that different experiments cannot use the same port).
• strategy: Choose a federated learning strategy, i.e., FedAvg, FedBN, FedRep and FedLPPA (FedUniv2.1).
• prompt: employ learnable prompts or one-hot. Two formats 'universal' or 'onehot'.
• attention: attention module selection.
• dual_init: Select a aggregated strategy.
• min_num_clients: The total number of clients.
• label_prompt: 0 or 1 , use or not use the sparse label prompts.
• role: 'Server' or 'Client'.
• cid: client_id.
• sup_type: Choose the format of sparse annotation.

5. Test the model

python -u test_client4onemod_FL_Personalize.py --client client1 --num_classes 2 --in_chns 1 --root_path ../data/FAZ_h5/test/ --img_class faz --exp faz/ --min_num_clients 5 --cid 1 --model unet_univ5

Other samples can be found in here.

Acknowledgement

• flower
• WSL4MIS
• FedALA
• FedLC

Citation

If you find FedLPPA useful in your research, please consider citing:

@article{lin2024fedlppa,
  title={FedLPPA: Learning Personalized Prompt and Aggregation for Federated Weakly-supervised Medical Image Segmentation},
  author={Lin, Li and Liu, Yixiang and Wu, Jiewei and Cheng, Pujin and Cai, Zhiyuan and Wong, Kenneth KY and Tang, Xiaoying},
  journal={arXiv preprint arXiv:2402.17502},
  year={2024}
}

If you have any questions, please feel free to contact us.