GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable
End-to-End Clinical Workflows in Medical Imaging
- URL: http://arxiv.org/abs/2103.01006v4
- Date: Tue, 16 May 2023 12:49:04 GMT
- Title: GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable
End-to-End Clinical Workflows in Medical Imaging
- Authors: Sarthak Pati, Siddhesh P. Thakur, \.Ibrahim Ethem Hamamc{\i}, Ujjwal
Baid, Bhakti Baheti, Megh Bhalerao, Orhun G\"uley, Sofia Mouchtaris, David
Lang, Spyridon Thermos, Karol Gotkowski, Camila Gonz\'alez, Caleb Grenko,
Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada,
Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng
Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia
Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah,
Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos
Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter
Mattson, Spyridon Bakas
- Abstract summary: We present the community-driven Generally Nuanced Deep Learning Framework (GaNDLF)
GaNDLF makes the mechanism of DL development, training, and inference more stable, reproducible, interpretable, and scalable.
We demonstrate the ability of GaNDLF to analyze both radiology and histology images, with built-in support for k-fold cross-validation.
- Score: 76.38169390121057
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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