Medigan

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medigan stands for medical generative (adversarial) networks. medigan provides user-friendly medical image synthesis and allows users to choose from a range of pretrained generative models to generate synthetic datasets. These synthetic datasets can be used to train or adapt AI models that perform clinical tasks such as lesion classification, segmentation or detection.

See below how medigan can be run from the command line to generate synthetic medical images.

Features:

• :x: Problem 1: Data scarcity in medical imaging.

• :x: Problem 2: Scarcity of readily reusable generative models in medical imaging.

• :white_check_mark: Solution: medigan

  1. dataset sharing via generative models :gift:
  2. data augmentation :gift:
  3. domain adaptation :gift:
  4. synthetic data evaluation method testing with multi-model datasets :gift:

Instead of training your own, use one of the generative models from medigan to generate synthetic data.

Search and find a model in medigan using search terms (e.g. "Mammography" or "Endoscopy").

Contribute your own generative model to medigan to increase its visibility, re-use, and impact.

Available models

| Output type | Modality | Model type | Output size | Base dataset | Output examples | model_id | Hosted on | Reference | |-------------------------------------------------------------|:-----------------------------:|:-----------------------------:|:-----------------------:|:--------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------:| | _{Breast Calcification} | _mammography | _dcgan | _128x128 | _Inbreast | | _00001_DCGAN_MMG_CALC_ROI | _{Zenodo (5187714)} | | | _{Breast Mass} | _mammography | _dcgan | _128x128 | _Optimam | | _00002_DCGAN_MMG_MASS_ROI | _{Zenodo (5188557)} | _{Alyafi et al (2019)} | | _{Breast Density Transfer} | _mammography | _cyclegan | _1332x800 | _BCDR | | _00003_CYCLEGAN_MMG_DENSITY_FULL | _{Zenodo (5547263)} | _{Garrucho et al (2022)} | | _{Breast Mass with Mask} | _mammography | _pix2pix | _256x256 | _BCDR | <br> | _{_00004_PIX2PIX_MMG_MASSES_W_MASKS} | _{Zenodo (7093759)} | | | _{Breast Mass} | _mammography | _dcgan | _128x128 | _BCDR | | _00005_DCGAN_MMG_MASS_ROI | _{Zenodo (6555188)} | _{Szafranowska et al (2022)} | | _{Breast Mass} | _mammography | _wgan-gp | _128x128 | _BCDR | | _00006_WGANGP_MMG_MASS_ROI | _{Zenodo (6554713)} | _{Szafranowska et al (2022)} | | _{Brain Tumors on Flair, T1, T1c, T2 with Masks} | _{brain MRI} | _{inpaint GAN} | _256x256 | <sub> [BRATS 2018](https://wiki.cancerimagingarchive.net/pages/viewpag