Practical overview
When it fits
Use Medigan when a medical-imaging workflow needs synthetic samples from available pretrained models, especially for the documented mammography generation and image-translation examples.
Who it is for
- Medical-imaging researchers exploring synthetic data for model development.
- Python developers building image-generation workflows with pretrained medical-imaging models.
- Teams investigating data augmentation, domain adaptation, or synthetic-data evaluation.
Documented uses
- Find pretrained models using keywords such as modality or model type.
- Generate synthetic medical-image samples or datasets with a selected model.
- Request a model's output as a PyTorch DataLoader.
- Explore synthetic data for training or adapting models for classification, segmentation, or detection.
Requirements & limits
The public repository identifies the project as open source under the MIT license and documents installation with pip or conda.
- Models vary by modality, task, architecture, output, and base dataset; inspect the model documentation and references before selecting one.
- The documented generation examples are model-specific, and the DCGAN example enables automatic installation of required model dependencies.
- The project is a Python library rather than a separately documented hosted generation service.
- Generated images are described as potential inputs for AI model training or adaptation, not as clinical outputs.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Medigan and generate mammography samples
Install the Python library and invoke the documented class-conditional mammography model to request synthetic samples.
Before you start
- Python environment with pip or conda available.
- Internet access may be needed to obtain the package and model dependencies; the example enables automatic installation of required model dependencies.
Steps
Install with the command below or
conda install -c conda-forge medigan.pip install mediganImport
Generatorsfrommediganand initialize it.Call
generators.generate(model_id=8, num_samples=8, install_dependencies=True)to request eight samples from the documented model.
Expected outcome
The example requests eight synthetic mammography mass images from model 8, identified as 00008_C-DCGAN_MMG_MASSES, with malignant or benign labels.
Guide limitations
- The example concerns this specific model and does not establish behavior or requirements for every model.
- The repository example is documentation, not an execution test.
Search the model collection by keywords
List available models and find entries whose configurations match selected terms.
Before you start
- Medigan installed in a Python environment.
- Python interpreter.
Steps
Import
Generatorsfrommediganand initialize it.Call
generators.list_models()to list models.Pass terms such as
['DCGAN', 'Mammography', 'BCDR']togenerators.find_matching_models_by_values(keywords).
Expected outcome
The example returns matching model entries for inspection.
Guide limitations
- Keyword search identifies configuration matches; the example does not describe a ranking method or validate a model's suitability for a research task.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 17 October 2026
- Original website title
- RichardObi/medigan · Official homepage title · retained in its source language