A loss-based patch label denoising method for improving whole-slide image analysis using a convolutional neural network

This paper proposes a deep learning-based patch label denoising method (LossDiff) for improving the classification of whole-slide images of cancer using a convolutional neural network (CNN). Automated whole-slide image classification is often challenging, requiring a large amount of labeled data. Pa...

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Detalles Bibliográficos
Autores principales: Ashraf, Murtaza, Robles, Willmer Rafell Quiñones, Kim, Mujin, Ko, Young Sin, Yi, Mun Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791954/
https://www.ncbi.nlm.nih.gov/pubmed/35082315
http://dx.doi.org/10.1038/s41598-022-05001-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791954/
https://www.ncbi.nlm.nih.gov/pubmed/35082315
http://dx.doi.org/10.1038/s41598-022-05001-8

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