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# Dafne
Deep Anatomical Federated Network is a program for the segmentation of medical images. It relies on a server to provide deep learning models to aid the segmentation, and incremental learning is used to improve the performance. See https://www.dafne.network/ for documentation and user information.
## Windows binary installation
Please install the Visual Studio Redistributable Package under windows: https://aka.ms/vs/16/release/vc_redist.x64.exe
Then, run the provided installer
## Mac binary installation
Install the Dafne App from the downloaded .dmg file as usual. Make sure to download the archive appropriate for your architecture (x86 or arm).
## Linux binary installation
The Linux distribution is a self-contained executable file. Simply download it, make it executable, and run it.
## pip installation
Dafne can also be installed with pip
`pip install dafne`
# Citing
If you are writing a scientific paper, and you used Dafne for your data evaluation, please cite the following paper:
> Santini F, Wasserthal J, Agosti A, et al. *Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation*. 2023 doi: [10.48550/arXiv.2302.06352](https://doi.org/10.48550/arXiv.2302.06352).
# Notes for developers
## dafne
Run:
`python dafne.py <path_to_dicom_img>`
## Notes for the DL models
### Apply functions
The input of the apply function is:
```
dict({
'image': np.array (2D image)
'resolution': sequence with two elements (image resolution in mm)
'split_laterality': True/False (indicates whether the ROIs should be split in L/R if applicable)
'classification': str - The classification tag of the image (optional, to identify model variants)
})
```
The output of the classifier is a string.
The output of the segmenters is:
```
dict({
roi_name_1: np.array (2D mask),
roi_name_2: ...
})
```
### Incremental learn functions
The input of the incremental learn functions are:
```
training data: dict({
'resolution': sequence (see above)
'classification': str (see above)
'image_list': list([
- np.array (2D image)
- np.array (2D image)
- ...
])
})
training outputs: list([
- dict({
roi_name_1: np.array (2D mask)
roi_name_2: ...
})
- dict...
```
Every entry in the training outputs list corresponds to an entry in the image_list inside the training data.
So `len(training_data['image_list']) == len(training_outputs)`.
# Acknowledgments
Input/Output is based on [DOSMA](https://github.com/ad12/DOSMA) - GPLv3 license
This software includes the [Segment Anything Model (SAM)](https://github.com/facebookresearch/segment-anything) - Apache 2.0 license
Other packages required for this project are listed in requirements.txt
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