GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging
Abstract
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.
Keywords
Cite
@article{arxiv.2103.01006,
title = {GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging},
author = {Sarthak Pati and Siddhesh P. Thakur and İbrahim Ethem Hamamcı and Ujjwal Baid and Bhakti Baheti and Megh Bhalerao and Orhun Güley and Sofia Mouchtaris and David Lang and Spyridon Thermos and Karol Gotkowski and Camila González and Caleb Grenko and Alexander Getka and Brandon Edwards and Micah Sheller and Junwen Wu and Deepthi Karkada and Ravi Panchumarthy and Vinayak Ahluwalia and Chunrui Zou and Vishnu Bashyam and Yuemeng Li and Babak Haghighi and Rhea Chitalia and Shahira Abousamra and Tahsin M. Kurc and Aimilia Gastounioti and Sezgin Er and Mark Bergman and Joel H. Saltz and Yong Fan and Prashant Shah and Anirban Mukhopadhyay and Sotirios A. Tsaftaris and Bjoern Menze and Christos Davatzikos and Despina Kontos and Alexandros Karargyris and Renato Umeton and Peter Mattson and Spyridon Bakas},
journal= {arXiv preprint arXiv:2103.01006},
year = {2023}
}
Comments
Deep Learning, Framework, Segmentation, Regression, Classification, Cross-validation, Data augmentation, Deployment, Clinical, Workflows