Segmentation of Nuclei in H&E-Stained Histological Images using Deep Learning Framework: A Perspective on Ensemble Approach and Nuclei Count-Reference-Cited by-同舟云学术

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Segmentation of Nuclei in H&E-Stained Histological Images using Deep Learning Framework: A Perspective on Ensemble Approach and Nuclei Count

Published:2023-10-16 Issue: Volume: Page:462-467
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Container-title:2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC)
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Author:

Aghera Madhavi¹,Singh Krishna Veer¹,Vaishnani Kishankumar¹,Oza Urvi¹,Gohel Bakul¹

Affiliation:

1. DAIICT,Gandhinagar,Gujarat,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10461726/10461727/10461806.pdf?arnumber=10461806

Reference22 articles.

1. Computational pathology: Challenges and promises for tissue analysis

2. Derivation of a nuclear heterogeneity image index to grade DCIS

3. Nucleus segmentation: towards automated solutions

4. Automatic extraction of cell nuclei from H&E-stained histopathological images

5. U-Net: Convolutional Networks for Biomedical Image Segmentation

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