A test implementation for the submitted paper "BPGAN: Bidirectional CT-to-MRI Prediction using Multi-Generative Multi-Adversarial Nets with Spectral Normalization and Localization" (Under reviewing)
python 3.6
pytorch (Pytorch http://pytorch.org/)
torchvision
numpy
time
pandas
scipy
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Download pre-trained models from BaiduNetdisk. password: ciw9.
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Download partial test samples from BaiduNetdisk, then put all this data into corresponding dir and extract compressed files.
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Copy the model (net_G_A: CT predictor, net_G_B: MRI predictor) into your dir
cp latest_net_G_A.pth ./brain_model/
cp latest_net_G_B.pth ./brain_model/
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Test for CT or MRI precition from MRI or CT images in proposed BPGAN
python test.py --dataroot ./datasets/brain --name brain_model
- The implementation of proposed BPGAN model is based on cycle-GAN (https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix). We improve the cycle-GAN by introducing pathological auxiliary information, spectral normalization, localization and edge retention to achieve the bidirectional prediction between CT and MRI images.
- This is developed on a Linux machine running Ubuntu 16.04.
- Use GPU for the high speed computation.
- Due to partial samples in SPLP dataset related to private information, so please e-mail me (xulimmail@gmail.com) if you need the dataset and I will share a private link with you.