Model Integration Request for medigan: 00018_WGANGP_XRAY_LUNG_NODULES

[RichardObi]

Creator: Eloy Garcia, Richard Osuala Affiliation: University of Barcelona Stored in: https://zenodo.org/record/6943762

Model Metadata:

   "00018_WGANGP_XRAY_LUNG_NODULES": {
      "execution": {
         "package_name": "00018_WGANGP_NODE21",
         "package_link": "https://zenodo.org/record/6943762",
         "model_name": "model",
         "extension": ".pt",
         "image_size": [
            128,
            128,
            1
         ],
         "dependencies": [
            "numpy",
            "tqdm",
            "torch"
         ],
         "generate_method": {
            "name": "generate",
            "args": {
               "base": [
                  "model_file",
                  "num_samples",
                  "output_path",
                  "save_images"
               ],
               "custom": {}
            },
            "input_latent_vector_size": 100
         }
      },
      "selection": {
         "performance": {
            "SSIM": null,
            "MSE": null,
            "NSME": null,
            "PSNR": null,
            "IS": null,
            "FID": null,
            "turing_test": null,
            "downstream_task": null
         },
         "use_cases": [
            "classification"
         ],
         "organ": [
            "lung",
            "chest",
            "thorax"
         ],
         "modality": [
            "x-ray",
            "xray",
            "CXR"
         ],
         "vendors": [],
         "centres": [],
         "function": [
            "noise to image",
            "unconditional generation",
            "data augmentation"
         ],
         "condition": [],
         "dataset": [
            "Node21"
         ],
         "augmentations": [
            "horizontal flip",
            "vertical flip"
         ],
         "generates": [
            "lung nodules",
            "nodules",
            "lung roi",
            "lung region of interest",
            "patches"
         ],
         "height": 128,
         "width": 128,
         "depth": 1,
         "type": "WGANGP",
         "license": "MIT",
         "dataset_type": "public",
         "privacy_preservation": null,
         "tags": [
            "Thoracic xray",
            "xray",
            "x-ray",
            "Thorax",
            "Lung",
            "Nodules",
            "Lung Cancer",
            "Lung Tumor"
         ],
         "year": "2022"
      },
      "description": {
         "title": "WGANGP Model for Patch Generation of Lung Nodules (Trained on Node21)",
         "provided_date": "June 2022",
         "trained_date": "June 2022",
         "provided_after_epoch": null,
         "version": null,
         "publication": null,
         "doi": [],
         "inputs": "",
         "comment": "A unconditional wasserstein generative adversarial network with gradient penalty (WGAN_GP) that generates lung nodule regions-of-interest patches based on chest xray (CXR) images. The pixel dimension of the generated patches is 128x128. The WGANGP was trained on cropped patches from CXR images from the NODE21 dataset (Sogancioglu et al, 2021). The uploaded ZIP file contains the files model.pt (model weight), __init__.py (image generation method and utils), a requirements.txt, a LICENSE file, the MEDIGAN metadata.json file, the used GAN training config file, a test.sh file to run the model, and an /image folder with a few generated example images."
      }
   }
}