打造通用生成框架，让医疗影像生成更易用、可复现。
GaNDLF-Synth: A Framework to Democratize Generative AI for (Bio)Medical Imaging
- 统一抽象多种生成算法，支持多模态数据
- 基于GANDLF-core实现分布式计算与可复现性
- 降低科研门槛，助力更多学者参与医疗生成研究
生成式人工智能（GenAI）利用深度学习从已有数据中生成新数据样本，有助于缓解医疗数据稀缺与监管限制问题。本文提出通用精细深度学习合成框架（GaNDLF-Synth），旨在填补文献空白，推动医疗影像合成任务的普及与评估。该框架统一抽象了自编码器、生成对抗网络和扩散模型等多种合成算法，基于GANDLF-core支持多模态数据与分布式计算，通过全面单元测试保障可扩展性与可复现性。其目标是降低生成式AI的使用门槛，使更广泛的科学界能便捷地开展医疗影像合成研究。
原文摘要 · Abstract (English)
Generative Artificial Intelligence (GenAI) is a field of AI that creates new data samples from existing ones. It utilizing deep learning to overcome the scarcity and regulatory constraints of healthcare data by generating new data points that integrate seamlessly with original datasets. This paper explores the background and motivation for GenAI, and introduces the Generally Nuanced Deep Learning Framework for Synthesis (GaNDLF-Synth) to address a significant gap in the literature and move towards democratizing the implementation and assessment of image synthesis tasks in healthcare. GaNDLF-Synth describes a unified abstraction for various synthesis algorithms, including autoencoders, generative adversarial networks, and diffusion models. Leveraging the GANDLF-core framework, it supports diverse data modalities and distributed computing, ensuring scalability and reproducibility through extensive unit testing. The aim of GaNDLF-Synth is to lower the entry barrier for GenAI, and make it more accessible and extensible by the wider scientific community.
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