Automatic Scoring Method for Tumor IHC Images Based on Deep Learning and Its Application on P53 Protein-Reference-Cited by-同舟云学术

Automatic Scoring Method for Tumor IHC Images Based on Deep Learning and Its Application on P53 Protein

Published:2023 Issue: Volume:11 Page:64005-64015
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zhang Jinbo¹^ORCID,Guo Xiaolei¹^ORCID,Cai Wen²,Cao Yuqi¹^ORCID,Ge Weiting²,Huang Pingjie¹,Hou Dibo¹^ORCID,Zheng Shu²,Zhang Guangxin¹

Affiliation:

1. School of Control Science and Engineering, Zhejiang University, Hangzhou, China

2. Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China

Funder

National Natural Science Foundation of China

“Pioneer” and “Leading Goose” Research and Development Program of Zhejiang

Key Technology Research and Development Program of Zhejiang Province

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/ielx7/6287639/10005208/10158676.pdf?arnumber=10158676

Reference34 articles.

1. FABNet: Fusion Attention Block and Transfer Learning for Laryngeal cancer Tumor Grading in P63 IHC Histopathology Images

2. Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification

3. OP-convNet: A Patch Classification-Based Framework for CT Vertebrae Segmentation

4. An Advanced Automated Image Analysis Model for Scoring of ER, PR, HER-2 and Ki-67 in Breast Carcinoma

5. Automated classification of protein expression levels in immunohistochemistry images to improve the detection of cancer biomarkers