Model Integration Request for medigan: 00013_C-DCGAN_MMG_MASSES

[RichardObi]

Creator: unknown name Affiliation: unknown affiliation Stored in: https://sandbox.zenodo.org/record/1076278

Model Metadata:

   "00013_C-DCGAN_MMG_MASSES": {
      "execution": {
         "package_name": "C-DCGAN_MMG_MASSES_BCDR_MAL_BEN",
         "package_link": "https://sandbox.zenodo.org/record/1076278",
         "model_name": "1250",
         "extension": ".pt",
         "image_size": [
            128,
            128
         ],
         "dependencies": [
            "numpy",
            "torch",
            "opencv-contrib-python-headless"
         ],
         "generate_method": {
            "name": "generate",
            "args": {
               "base": [
                  "model_file",
                  "num_samples",
                  "output_path",
                  "save_images"
               ],
               "custom": {
                  "condition": null,
                  "z": null
               }
            },
            "input_latent_vector_size": 100
         }
      },
      "selection": {
         "performance": {
            "SSIM": null,
            "MSE": null,
            "NSME": null,
            "PSNR": null,
            "IS": null,
            "FID": null,
            "turing_test": null,
            "downstream_task": {
               "CLF": {}
            }
         },
         "use_cases": [
            "classification",
            "malignant versus benign classification"
         ],
         "organ": [
            "breast",
            "breasts",
            "chest"
         ],
         "modality": [
            "MMG",
            "Mammography",
            "Mammogram",
            "full-field digital",
            "full-field digital MMG",
            "full-field MMG",
            "full-field Mammography",
            "digital Mammography",
            "digital MMG",
            "x-ray mammography"
         ],
         "vendors": [],
         "centres": [],
         "function": [
            "noise to image",
            "image generation",
            "unconditional generation",
            "data augmentation"
         ],
         "condition": [],
         "dataset": [
            "CBIS-DDSM"
         ],
         "augmentations": [
            "horizontal flip",
            "vertical flip"
         ],
         "generates": [
            "mass",
            "masses",
            "mass roi",
            "mass ROI",
            "mass images",
            "mass region of interest",
            "nodule",
            "nodule",
            "nodule roi",
            "nodule ROI",
            "nodule images",
            "nodule region of interest"
         ],
         "height": 128,
         "width": 128,
         "depth": null,
         "type": "Conditional DCGAN",
         "license": "MIT",
         "dataset_type": "public",
         "privacy_preservation": null,
         "tags": [
            "Breast",
            "Mammogram",
            "Mammography",
            "Digital Mammography",
            "Full field Mammography",
            "Full-field Mammography",
            "128x128",
            "128 x 128",
            "MammoGANs",
            "Masses",
            "Nodules"
         ],
         "year": "2022"
      },
      "description": {
         "title": "Conditional DCGAN Model for Patch Generation of Mammogram Masses Conditioned on Biopsy Proven Malignancy Status (Trained on BCDR)",
         "provided_date": "June 2022",
         "trained_date": "June 2022",
         "provided_after_epoch": 1250,
         "version": "1.0.0",
         "publication": null,
         "doi": [],
         "comment": "A class-conditional deep convolutional generative adversarial network that generates mass patches of mammograms that are conditioned to either be benign (1) or malignant (0). Pixel dimensions are 128x128. The Cond-DCGAN was trained on MMG patches from the BCDR dataset (Lopez et al, 2012). The uploaded ZIP file contains the files 1250.pt (model weight), __init__.py (image generation method and utils), a requirements.txt, a LICENSE file, the MEDIGAN metadata, the used GAN training config file, a test.sh file to run the model, and two folders with a few generated images."
      }
   }
}