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--- |
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license: mit |
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tags: |
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- 'GAN ' |
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- Image gen |
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- Medical |
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- CT-scan |
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- CT |
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--- |
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## Model Details |
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Generative AI has many practical uses. One important use is synthetic data generation. |
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And why is synthetic data useful? Well, without it any model trained on medical image data probably has to |
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use real images. And this has some implications when it comes to patient privacy. So to help mitigate this risk, |
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synthetic data generators are a great tool. |
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They are initially trained on real data, but the data they produce can be used to train other models, |
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eliminating the need to use the real patient data. |
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I decided to use a GAN (Generative Adverserial Network) model for this task. |
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It consists of a generator to create images and a discriminator to determine whether the images are fake or not. |
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And as the generator gets better and better at creating new images, the discriminator has more difficulty distinguishing them. |
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The images generated are CT scans of the lungs, either cancerous or non-cancerous. |
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I used this for reference, and adapted it to my own use case. |
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https://colab.research.google.com/github/pytorch/tutorials/blob/gh-pages/_downloads/dcgan_faces_tutorial.ipynb |
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## Uses |
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This model is a proof of concept, that shows it is possible to create good synthetic image data. Which in turn can be used to train |
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other models, eventually reducing the need for using actual patient data. |
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### Out-of-Scope Use |
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This model has not ben evaluated for actual medical use or diagnosis of any kind. |
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## Bias, Risks, and Limitations |
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Not trained on data that has been evaluated for balance. It was simply a proof of concept. |
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The data originally comes from the Imaging Data commons of the NIH, |
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https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=nlst |
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Citation: |
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Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. Cancer Res. 81, 4188–4193 (2021). |
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http://dx.doi.org/10.1158/0008-5472.CAN-21-0950 https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=nlst |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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This is a proof of concept only |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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```python |
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import torch |
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from huggingface_hub import hf_hub_download |
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from torch import nn |
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# Generator Code |
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from huggingface_hub import hf_hub_download |
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from torch import nn |
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from torchvision.utils import save_image |
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class Generator(nn.Module): |
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def __init__(self, ngpu=1, ngf=128, nz=128, nc =1): |
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super(Generator, self).__init__() |
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self.ngpu = ngpu |
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self.main = nn.Sequential( |
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nn.ConvTranspose2d(nz, ngf * 16, 4, 1, 0, bias=False), |
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nn.BatchNorm2d(ngf*16), |
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nn.LeakyReLU(0.2, inplace=True), |
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nn.ConvTranspose2d(ngf*16, ngf*8, 4, 2, 1, bias=False), |
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nn.BatchNorm2d(ngf * 8), |
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nn.LeakyReLU(0.2, inplace=True), |
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nn.ConvTranspose2d(ngf*8, ngf*4, 4, 2, 1, bias=False), |
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nn.BatchNorm2d(ngf*4), |
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nn.ReLU(True), |
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nn.ConvTranspose2d(ngf*4, ngf*2, 4, 2, 1, bias=False), |
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nn.BatchNorm2d(ngf * 2), |
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nn.ReLU(True), |
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nn.ConvTranspose2d(ngf*2,ngf, 4, 2, 1, bias=False), |
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nn.BatchNorm2d(ngf), |
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nn.ReLU(True), |
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nn.ConvTranspose2d(ngf,nc, 4, 2, 1, bias=False), |
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nn.Tanh() |
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) |
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def forward(self, input): |
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return self.main(input) |
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model = Generator() |
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weights_path = hf_hub_download('oohtmeel/ct-gan-gen', 'lung_ct_generator_gan.pth') |
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model.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) |
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out = model(torch.randn(128, 128, 1, 1)) |
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save_image(out, "ct_scans.png", normalize=True) |
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``` |
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## Model Card Contact |
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[[email protected]] |
