Pati, Sarthak ; Thakur, Siddhesh P. ; Hamamcı, İbrahim Ethem ; Baid, Ujjwal ; Baheti, Bhakti ; Bhalerao, Megh ; Güley, Orhun ; Mouchtaris, Sofia ; Lang, David ; Thermos, Spyridon ; Gotkowski, Karol ; González, Camila ; Grenko, Caleb ; Getka, Alexander ; Edwards, Brandon ; Sheller, Micah ; Wu, Junwen ; Karkada, Deepthi ; Panchumarthy, Ravi ; Ahluwalia, Vinayak ; Zou, Chunrui ; Bashyam, Vishnu ; Li, Yuemeng ; Haghighi, Babak ; Chitalia, Rhea ; Abousamra, Shahira ; Kurc, Tahsin M. ; Gastounioti, Aimilia ; Er, Sezgin ; Bergman, Mark ; Saltz, Joel H. ; Fan, Yong ; Shah, Prashant ; Mukhopadhyay, Anirban ; Tsaftaris, Sotirios A. ; Menze, Bjoern ; Davatzikos, Christos ; Kontos, Despina ; Karargyris, Alexandros ; Umeton, Renato ; Mattson, Peter ; Bakas, Spyridon (2023)
GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows.
In: Communications Engineering, 2 (1)
doi: 10.1038/s44172-023-00066-3
Artikel, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Pati, Sarthak ; Thakur, Siddhesh P. ; Hamamcı, İbrahim Ethem ; Baid, Ujjwal ; Baheti, Bhakti ; Bhalerao, Megh ; Güley, Orhun ; Mouchtaris, Sofia ; Lang, David ; Thermos, Spyridon ; Gotkowski, Karol ; González, Camila ; Grenko, Caleb ; Getka, Alexander ; Edwards, Brandon ; Sheller, Micah ; Wu, Junwen ; Karkada, Deepthi ; Panchumarthy, Ravi ; Ahluwalia, Vinayak ; Zou, Chunrui ; Bashyam, Vishnu ; Li, Yuemeng ; Haghighi, Babak ; Chitalia, Rhea ; Abousamra, Shahira ; Kurc, Tahsin M. ; Gastounioti, Aimilia ; Er, Sezgin ; Bergman, Mark ; Saltz, Joel H. ; Fan, Yong ; Shah, Prashant ; Mukhopadhyay, Anirban ; Tsaftaris, Sotirios A. ; Menze, Bjoern ; Davatzikos, Christos ; Kontos, Despina ; Karargyris, Alexandros ; Umeton, Renato ; Mattson, Peter ; Bakas, Spyridon |
| Art des Eintrags: | Bibliographie |
| Titel: | GaNDLF: the generally nuanced deep learning framework for scalable end-to-end clinical workflows |
| Sprache: | Englisch |
| Publikationsjahr: | 16 Mai 2023 |
| Verlag: | Springer Nature |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Communications Engineering |
| Jahrgang/Volume einer Zeitschrift: | 2 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1038/s44172-023-00066-3 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | Deep learning, Radiology, Pathology, Medical diagnosis |
| Zusätzliche Informationen: | Art.No.: 23 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 04 Dez 2023 12:34 |
| Letzte Änderung: | 30 Jan 2024 13:53 |
| PPN: | 515140449 |
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| Suche nach Titel in: | TUfind oder in Google |
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