  Deep Learning (DL) has the potential to optimize machine learning in both the
scientific and clinical communities. However, greater expertise is required to
develop DL algorithms, and the variability of implementations hinders their
reproducibility, translation, and deployment. Here we present the
community-driven Generally Nuanced Deep Learning Framework (GaNDLF), with the
goal of lowering these barriers. GaNDLF makes the mechanism of DL development,
training, and inference more stable, reproducible, interpretable, and scalable,
without requiring an extensive technical background. GaNDLF aims to provide an
end-to-end solution for all DL-related tasks in computational precision
medicine. We demonstrate the ability of GaNDLF to analyze both radiology and
histology images, with built-in support for k-fold cross-validation, data
augmentation, multiple modalities and output classes. Our quantitative
performance evaluation on numerous use cases, anatomies, and computational
tasks supports GaNDLF as a robust application framework for deployment in
clinical workflows.
