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

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...

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Bibliographic Details
Main Authors: Chuan Zhou, Heang-Ping Chan, Lubomir M. Hadjiiski, Aamer Chughtai
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Deep learning
U-Net model
pathology images
image segmentation
Online Access:https://ieeexplore.ieee.org/document/9770035/

Internet

https://ieeexplore.ieee.org/document/9770035/

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