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

ABSTRACT:

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 implemented as an open-source framework-agnostic Python library. After gathering end-user requirements, design decisions based on usability, te

SUBMITTER: Osuala R 

PROVIDER: S-EPMC9940031 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature