Improving Generalization of Deep Learning Models for Diagnostic Pathology by Increasing Variability in Training Data: Experiments on Osteosarcoma Subtypes

BACKGROUND: Artificial intelligence has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting a...

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Detalles Bibliográficos
Autores principales: Tang, Haiming, Sun, Nanfei, Shen, Steven
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404558/
https://www.ncbi.nlm.nih.gov/pubmed/34497734
http://dx.doi.org/10.4103/jpi.jpi_78_20

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8404558/
https://www.ncbi.nlm.nih.gov/pubmed/34497734
http://dx.doi.org/10.4103/jpi.jpi_78_20

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