Multi-Configuration Analysis of DenseNet Architecture for Whole Slide Image Scoring of ER-IHC-Reference-Cited by-同舟云学术

Multi-Configuration Analysis of DenseNet Architecture for Whole Slide Image Scoring of ER-IHC

Published:2023 Issue: Volume:11 Page:79911-79928
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Ahmad Wan Siti Halimatul Munirah Wan¹^ORCID,Ahmad Fauzi Mohammad Faizal¹^ORCID,Hasan Md Jahid¹^ORCID,Rehman Zaka Ur¹^ORCID,Lee Jenny Tung Hiong²,Khor See Yee³,Looi Lai-Meng⁴^ORCID,Abas Fazly Salleh⁵^ORCID,Adam Afzan⁶^ORCID,Chan Elaine Wan Ling⁷^ORCID,Kamata Sei-Ichiro⁸^ORCID

Affiliation:

1. Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

2. Department of Pathology, Sarawak General Hospital, Kuching, Sarawak, Malaysia

3. Hospital Seberang Jaya, Seberang Jaya, Penang, Malaysia

4. Department of Pathology, University Malaya Medical Center, Kuala Lumpur, Malaysia

5. Faculty of Engineering and Technology, Multimedia University, Ayer Keroh, Malacca, Malaysia

6. Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia

7. Fusionex AI Laboratory, International Medical University, Kuala Lumpur, Malaysia

8. Kamata Laboratory, Waseda University, Kitakyushu-shi, Japan

Funder

Ministry of Higher Education (MOHE) Malaysia through the Research Excellence Consortium, Artificial Intelligence for Digital Pathology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10187125.pdf?arnumber=10187125

Reference38 articles.

1. Introduction to digital image analysis in whole-slide imaging: A white paper from the digital pathology association

2. Nuclei Classification in ER-IHC Stained Histopathology Images using Deep Learning Models

3. Display characteristics and their impact on digital pathology: A current review of pathologists' future “microscope”

4. Collaborative analysis of multi-gigapixel imaging data using Cytomine

5. Image analysis and machine learning in digital pathology: Challenges and opportunities

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