Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology

Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology

Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, ope...

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Bibliographic Details
Main Authors: Henrik Sahlin Pettersen, Ilya Belevich, Elin Synnøve Røyset, Erik Smistad, Melanie Rae Simpson, Eija Jokitalo, Ingerid Reinertsen, Ingunn Bakke, André Pedersen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-01-01
Series:Frontiers in Medicine
Subjects:
computational pathology
deep learning
code-free
semantic segmentation
U-Net
open datasets
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2021.816281/full

Internet

https://www.frontiersin.org/articles/10.3389/fmed.2021.816281/full

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