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Titel:

Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology.

Dokumenttyp:
Journal Article; Review
Autor(en):
Wagner, Sophia J; Matek, Christian; Shetab Boushehri, Sayedali; Boxberg, Melanie; Lamm, Lorenz; Sadafi, Ario; Winter, Dominik J E; Marr, Carsten; Peng, Tingying
Abstract:
Recent progress in computational pathology has been driven by deep learning. While code and data availability are essential to reproduce findings from preceding publications, ensuring a deep learning model's reusability is more challenging. For that, the codebase should be well-documented and easy to integrate into existing workflows and models should be robust toward noise and generalizable toward data from different sources. Strikingly, only a few computational pathology algorithms have been r...     »
Zeitschriftentitel:
Mod Pathol
Jahr:
2024
Band / Volume:
37
Heft / Issue:
1
Volltext / DOI:
doi:10.1016/j.modpat.2023.100350
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/37827448
Print-ISSN:
0893-3952
TUM Einrichtung:
Institut für Allgemeine Pathologie und Pathologische Anatomie (Dr. Mogler komm.)
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