This repository contains the implementation for PregGAN. Based on the characteristics of GAN, we developed its ability as a disease prognosis prediction model, and designed PregGAN to predict the survival time of breast cancer patients.
The structure diagram of PregGAN is shown in the figure below.
The hyperparameters of the neural network in the generator and discriminator of PregGAN are shown in the following table.
| Layer | Detail | Input Sizes | Output sizes | |
|---|---|---|---|---|
| Fully connected layer | BatchNorm,ReLU | 50 | 64 | |
| Fully connected layer | BatchNorm,ReLU | 64 | 128 | |
| Generator | Fully connected layer | BatchNorm,ReLU | 128 | 64 |
| Fully connected layer | 64 | 1 | ||
| Sigmoid | 1 | 1 | ||
| Fully connected layer | BatchNorm,LeakyReLU | 31 | 64 | |
| Discriminator | Fully connected layer | BatchNorm,LeakyReLU | 64 | 128 |
| Fully connected layer | BatchNorm,LeakyReLU | 128 | 64 | |
| Fully connected layer | 64 | 1 |
The environment can be set up using Anaconda with the following commands:
conda create --name preggan-pytorch python=3.6
conda activate preggan-pytorch
pip install torch==1.8.2+cu102 torchvision==0.9.2+cu102 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
pip install -r requirements.txt
cd ..\PregGAN
jupyter notebook
- Run the code blocks in order after opening the jupyter notebook.