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https://github.com/albarqounilab/FedDis-NMI
The source code of FedDis
https://github.com/albarqounilab/FedDis-NMI
deep-learning federated-learning medical-imaging
Last synced: 14 days ago
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The source code of FedDis
- Host: GitHub
- URL: https://github.com/albarqounilab/FedDis-NMI
- Owner: albarqounilab
- License: other
- Created: 2022-06-01T18:24:55.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-06-03T06:40:39.000Z (over 2 years ago)
- Last Synced: 2024-07-31T20:40:24.508Z (3 months ago)
- Topics: deep-learning, federated-learning, medical-imaging
- Language: Python
- Homepage:
- Size: 449 KB
- Stars: 17
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
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# FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
### Cosmin I Bercea, Benedikt Wiestler, Daniel Rueckert, and [Shadi Albarqouni](https://albarqouni.github.io/)
This repository contains the code for our paper on [FedDis: Federated Disentangled Representation Learning for Unsupervised Brain Anomaly Detection](https://assets.researchsquare.com/files/rs-722389/v1_covered.pdf?c=1631875543).
If you use any of our code, please cite:
```
@article{bercea2021feddis,
title={FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation},
author={Bercea, Cosmin I and Wiestler, Benedikt and Rueckert, Daniel and Albarqouni, Shadi},
journal={arXiv preprint arXiv:2103.03705},
year={2021}
}
```
* [FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation](#fedis:-disentangled-federated-learning-for-unsupervised-brain-pathology-segmentation)
* [Requirements](#requirements)
* [Folder Structure](#folder-structure)
* [Usage](#usage)
* [Data](#data)
* [Config file format](#config-file-format)
* [Usage](#cli-usage)
* [Disclaimer](#disclaimer)
* [License](#license)
## Requirements
* Python >= 3.6
* This project is based on the federated framework [ILIA](https://github.com/albarqounilab/ILIA) and
the [MONAI](https://monai.io) framework
All additional packages used in this repository are listed in [requirements.txt](https://github.com/albarqounilab/FedDis/blob/main/requirements.txt).
To install those, run `pip3 install -r requirements.txt`
## Folder Structure
```
FedDis/
│
├── feddis_main.py - execute to run in commandline
├── FedAnalytics.py - Stats and evaluations at the end of epochs/training (not implemented)
├── FedDownstreamTask.py - Downstream evaluations for anomaly segmentation and reconstruction fidelity
├── FedPlanner.py - Plans and starts the experimens
├── FedVizu.py - Visuailzations at the end of epochs/training (not implemented)
MONAI/monai/
├── ILIA/- Federated framework based on PyTorch
│ ├── core/ - Configuration of the project and experiment
│ ├── ├── FedAggregator/ - Moderates federated traing / Aggregates local updates
│ ├── ├── FedAnalytics/ - Stats and evaluations at the end of epochs/training
│ ├── ├── FedCollaborator/ - Client-side training and testing
│ ├── ├── FedDownstreamTask/ - Downstream evaluations, e.g., Anomaly Detection
│ ├── ├── FedPlanner/ - Plans and starts the experimens
│ ├── ├── FedVizu/ - Visuailzations at the end of epochs/training
│ ├── data/ - dataloaders
│ ├── models/ - Configuration of different federated methods, e.g., FedAvg, FedDis, SiloBN, etc..
│
├── losses/ - Definition of losses
├── metrics/ - Definition of metrics
├── networks/ - Definition of networks (e.g., autoencoder.py)
├── optimizers/ - Definition of optimizers
├── transforms/ - Definition of transforms
├── utils/ - Definition of utils
```
## Usage
### Data
The publicly available healthy training data used can be downloaded here:
[OASIS-SI](https://www.oasis-brains.org)
[ADNI-SI and ADNI-Ph](http://adni.loni.usc.edu/data-samples/access-data/)
The publicly available datasets containing anomaly can be downloaded here:
[MSLUB](http://lit.fe.uni-lj.si/tools.php?lang=eng)
[MSISBI](https://smart-stats-tools.org/lesion-challenge-2015)
[WMH](https://wmh.isi.uu.nl)
[BRATS](https://www.med.upenn.edu/sbia/brats2018/data.html)
### Config file format
Define the config file including the desired network, datasets and training hyper-parameters, see the example provided
config/feddis_config.yaml.
If you want to use your own dataset, check how the dataloaders in `MONAI/monai/ILIA/data/BrainMR/anomaly_detection.py`
are defined and implement your own to work with our code.
```
## YAML CONFIG FILE
name: FedDis
experiment:
name: ILIA-test
task: test
models:
client_OASIS: '/path_to_checkpoint/checkpoints/client_OASIS_best_global_model.pt'
client_ADNI-S: '/path_to_checkpoint/checkpoints/client_ADNI-S_best_global_model.pt'
client_ADNI-P: '/path_to_checkpoint/checkpoints/client_ADNI-P_best_global_model.pt'
client_KRI: '/path_to_checkpoint/checkpoints/client_KRI_best_global_model.pt'
logging: 1
device: gpu
model:
module_name: monai.networks.nets.disentangled_autoencoder
class_name: DisAutoEncoder
params:
intermediate_res: !!python/tuple [16,16]
filters_start_shape: 64
filters_max_shape: 128
filters_start_app: 16
filters_max_app: 32
use_batchnorm: True
collaborator:
module_name: monai.ILIA.models.FedDis.FedCollaborator
class_name: FedDisCollaborator
names:
- OASIS
- ADNI-S
- ADNI-P
- KRI
params:
nr_epochs: 5
max_iterations: 1000 # FedVC
checkpoint_path: '/path_to_checkpoint/Ilia-test/'
data_loader:
module_name: monai.ILIA.data.BrainMR.anomaly_detection
class_name: NatureHealthyDataLoader
params:
image_size: !!python/tuple [256, 256]
target_size: !!python/tuple [128, 128]
slice_range: !!python/tuple [70, 100]
batch_size: 8
self_supervision:
use: True
params:
start_x_range: !!python/tuple [30, 90]
start_y_range: !!python/tuple [20, 90]
width_range: !!python/tuple [3, 20]
max_rectangles: 3
cval: -1 # -1 = random hyper-intensity
optimizer_params:
lambda_R: 0.2
lambda_S: 1
lambda_L: 1
round_scl_injection: 25
decay_rate: 0.97
learning_rate: 0.0001
gamma_shift: !!python/tuple [0.5, 2]
use_shape_pair: False
aggregator:
module_name: monai.ILIA.models.FedDis.FedAggregator
class_name: FedDisAggregator
params:
nr_rounds: 50
client_num_in_total: 4
client_num_per_round: 4
test_frequency: 3
checkpoint_path: '/path_to_checkpoint/Ilia-test/'
test_data:
data_loader_healthy:
dataset_names:
- OASIS
- ADNI-S
- ADNI-P
- KRI
module_name: monai.ILIA.data.BrainMR.anomaly_detection
class_name: NatureHealthyDataLoader
params:
image_size: !!python/tuple [256, 256]
target_size: !!python/tuple [128, 128]
slice_range: !!python/tuple [70, 100]
batch_size: 8
data_loader_anomaly:
dataset_names:
- MSLUB
- MSISBI
- MSKRI
- GBKRI
- BRATS
module_name: monai.ILIA.data.BrainMR.anomaly_detection
class_name: NatureAnomalyDataLoader
params:
image_size: !!python/tuple [256, 256]
target_size: !!python/tuple [128, 128]
slice_range: !!python/tuple [15, 125]
batch_size: 1
downstream_tasks:
module_name: monai.ILIA.projects.nature_mi_21.FedDownstreamTask
class_name: FedDisDownstreamTask
params:
analytics:
module_name: monai.ILIA.projects.nature_mi_21.FedAnalytics
class_name: FedDisAnalytics
params:
vizu:
module_name: monai.ILIA.projects.nature_mi_21.FedVizu
class_name: FedDisVizu
params:
```
### Usage
For the results of our paper we used `feddis_main.py`.
If you wish just to evaluate an already trained model, set the experiment/task in the config file to test and link to
the saved model parameters.
## Disclaimer
The code has been cleaned up and polished for clarity and reproducibility. While the code has been thoroughly reviewed, it may contain bugs or errors. Please contact [email protected] if you encounter any problem in this repository.
## License
This project is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0). See [LICENSE](https://github.com/albarqounilab/FedDis-NMI/blob/main/LICENSE.md) for more details