Recursive Training Strategy for a Deep Learning Network for Segmentation of Pathology Nuclei With Incomplete Annotation

This study developed a recursive training strategy to train a deep learning model for nuclei detection and segmentation using incomplete annotation. A dataset of 141 H&E stained breast cancer pathologic images with incomplete annotation was randomly split into training/validation set and test se...

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Detalles Bibliográficos
Autores principales: ZHOU, CHUAN, CHAN, HEANG-PING, HADJIISKI, LUBOMIR M., CHUGHTAI, AAMER
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161776/
https://www.ncbi.nlm.nih.gov/pubmed/35665366
http://dx.doi.org/10.1109/access.2022.3172958

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9161776/
https://www.ncbi.nlm.nih.gov/pubmed/35665366
http://dx.doi.org/10.1109/access.2022.3172958

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