https://github.com/divelab/completion
https://github.com/divelab/completion
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/divelab/completion
- Owner: divelab
- License: mit
- Created: 2018-02-08T00:28:15.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-10-09T14:24:43.000Z (over 6 years ago)
- Last Synced: 2025-04-05T02:21:51.512Z (about 1 year ago)
- Language: Python
- Size: 1.56 MB
- Stars: 15
- Watchers: 0
- Forks: 3
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Deep Adversarial Learning for Multi-Modality Missing Data Completion
Deep Adversarial Learning for Multi-Modality Missing Data Completion can be used to generate missing modality using the existing modality.
Detailed information is provided in paper (https://dl.acm.org/citation.cfm?id=3219963).
## Citation
If using this code, please cite our paper.
```
@inproceedings{cai2018deep,
title={Deep adversarial learning for multi-modality missing data completion},
author={Cai, Lei and Wang, Zhengyang and Gao, Hongyang and Shen, Dinggang and Ji, Shuiwang},
booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={1158--1166},
year={2018},
organization={ACM}
}
```
## System requirement
#### Programming language
Python
#### Python Packages
tensorflow (CPU) or tensorflow-gpu (GPU), numpy, h5py, progressbar, PIL, scipy
## Prepare data
We use ANDI dataset in our paper. Please download the dataset and process the data following the paper. The processed data shoud be saved in h5 format. Both training and testing h5 file should contains three keys ('label', 'mri', 'pet'). The shape of 'mri' and 'pet' in h5 file should be NxDxHxWxC. In adni dataset, we process the data as D=H=W=64, C=1. The shape of 'label' in h5 file shoud be Nx1.
## Configure the network
All network hyperparameters are configured in main.py.
#### Training
max_step: how many iterations or steps to train
test_step: how many steps to perform a mini test or validation
save_step: how many steps to save the model
summary_step: how many steps to save the summary
trade_off: trade of MSE loss and adversarial loss
#### Data
data_dir: data directory
train_data: h5 file for training
valid_data: h5 file for validation
test_data: h5 file for testing
batch: batch size
channel: input image channel number
height, width: height and width of input image
#### Debug
logdir: where to store log
modeldir: where to store saved models
sampledir: where to store predicted samples, please add a / at the end for convinience
model_name: the name prefix of saved models
reload_step: where to return training
test_step: which step to test or predict
random_seed: random seed for tensorflow
#### Network architecture
network_depth: how deep of the U-Net including the bottom layer
start_channel_num: the number of channel for the first conv layer
## Training
#### Start training
After configure the network, we can start to train. Run
```
python main.py
```
## Acknowledgments
Part of code borrows from [PixelDCL](https://github.com/HongyangGao/PixelDCN). Thanks for their excellent work!