Deep Learning (DL) has greatly highlighted the potential impact of optimized machine learning in both the scientific and clinical communities. The advent of open-source DL libraries from major industrial entities, such as TensorFlow (Google), PyTorch (Facebook), and MXNet (Apache), further contributes to DL promises on the democratization of computational analytics. However, increased technical and specialized background is required to develop DL algorithms, and the variability of implementation details hinders their reproducibility. Towards lowering the barrier and making the mechanism of DL development, training, and inference more stable, reproducible, and scalable, without requiring an extensive technical background, this manuscript proposes the Generally Nuanced Deep Learning Framework (GaNDLF). With built-in support for $k$-fold cross-validation, data augmentation, multiple modalities and output classes, and multi-GPU training, as well as the ability to work with both radiographic and histologic imaging, GaNDLF aims to provide an end-to-end solution for all DL-related tasks, to tackle problems in medical imaging and provide a robust application framework for deployment in clinical workflows.
翻译:深度学习(DL)极大地凸显了科学界和临床界优化机器学习的潜在影响。来自主要工业实体,如TensorFlow(Google)、PyTorrch(Facebook)和MXNet(Apache)等的开放源源DL图书馆的出现,进一步促成了DL关于计算分析民主化的承诺。然而,开发DL算法需要增加技术和专门背景,执行细节的变异性阻碍其再生。降低障碍,使DL开发、培训和推断机制更加稳定、可复制和可缩放,不需要广泛的技术背景,本稿提出了通用Nual Nuance深学习框架(GANDLF ), 以内在支持美元乘法交叉校验、数据增强、多种模式和产出班以及多GPU培训,以及能够与辐射学及其tologimma一起工作,GANNDLF旨在为与所有DL部署有关的临床工作流程提供最终解决方案。