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Medigan: A Python library of pretrained generative models for medical image synthesis

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Purpose: 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.
Approach: We propose medigan, a one-stop shop for pretrained generative models imple-
mented 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 medigan based on modular components for generative model
(i) execution, (ii) visualization, (iii) search & ranking, and (iv) contribution. We integrate pre-
trained models with applications across modalities such as mammography, endoscopy, x-ray,
and MRI.
Results: 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 medigan 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.
Conclusion: medigan 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 medigan’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.

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Society of Photo-Optical Instrumentation Engineers (SPIE)

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Journal of Medical Imaging, 2023, vol. 10, num.6

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Articles publicats en revistes (Matemàtiques i Informàtica)

# metadata.dc.title[].value
Medigan: A Python library of pretrained generative models for medical image synthesis