medigan : a Python library of pretrained generative models for medical image synthesis

• Published in: Journal of medical imaging (Bellingham, Wash.) Wash.), 2023-11, Vol.10 (6), p.061403
• Main Authors: Osuala, Richard, Skorupko, Grzegorz, Lazrak, Noussair, Garrucho, Lidia, García, Eloy, Joshi, Smriti, Jouide, Socayna, Rutherford, Michael, Prior, Fred, Kushibar, Kaisar, Díaz, Oliver, Lekadir, Karim
• Format: Article
• Language: English
• Subjects: Special Section on Informatics and Imaging Data Management
• ISSN: 2329-4302; 2329-4310
• DOI: 10.1117/1.JMI.10.6.061403

Deep learning has shown great promise as the backbone of clinical decision support systems. Synthetic data generated by generative models can enhance the performance and capabilities of data-hungry deep learning models. However, there is (1) limited availability of (synthetic) datasets and (2) generative models are complex to train, which hinders their adoption in research and clinical applications. To reduce this entry barrier, we explore generative model sharing to allow more researchers to access, generate, and benefit from synthetic data. We propose , a one-stop shop for pretrained generative models implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, technical feasibility, and scalability are formulated. Subsequently, we implement based on modular components for generative model (i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pretrained models with applications across modalities such as mammography, endoscopy, x-ray, and MRI. The scalability and design of the library are demonstrated by its growing number of integrated and readily-usable pretrained generative models, which include 21 models utilizing nine different generative adversarial network architectures trained on 11 different datasets. We further analyze three applications, which include (a) enabling community-wide sharing of restricted data, (b) investigating generative model evaluation metrics, and (c) improving clinical downstream tasks. In (b), we extract Fréchet inception distances (FID) demonstrating FID variability based on image normalization and radiology-specific feature extractors. allows researchers and developers to create, increase, and domain-adapt their training data in just a few lines of code. Capable of enriching and accelerating the development of clinical machine learning models, we show 's viability as platform for generative model sharing. Our multimodel synthetic data experiments uncover standards for assessing and reporting metrics, such as FID, in image synthesis studies.