EMTL-Net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy-Reference-Cited by-同舟云学术

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EMTL-Net: Boosting segmentation quality in histopathology images of gland and nuclei by explainable multitask learning network as an optimized strategy

Published:2024-03 Issue: Volume:51 Page:101636
ISSN:2215-0986
Container-title:Engineering Science and Technology, an International Journal
language:en
Short-container-title:Engineering Science and Technology, an International Journal

Author:

Ali Haider,Wang Mingzhao,Xie Juanying^ORCID

Funder

National Natural Science Foundation of China

Shaanxi Province Postdoctoral Science Foundation

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Reference60 articles.

1. A new initiative on precision medicine;Collins;New Engl. J. Med.,2015

2. Dataset of segmented nuclei in hematoxylin and eosin stained histopathology images of ten cancer types;Hou;Sci. Data,2020

3. Diagnostic concordance among pathologists interpreting breast biopsy specimens;Elmore;JAMA,2015

4. A dataset for breast cancer histopathological image classification;Spanhol;IEEE Trans. Biomed. Eng.,2015

5. Deep learning with sampling in colon cancer histology;Shapcott;Front. Bioeng. Biotechnol.,2019

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