A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images-Reference-Cited by-同舟云学术

A ViT-AMC Network With Adaptive Model Fusion and Multiobjective Optimization for Interpretable Laryngeal Tumor Grading From Histopathological Images

Published:2023-01 Issue:1 Volume:42 Page:15-28
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Huang Pan¹^ORCID,He Peng¹^ORCID,Tian Sukun²^ORCID,Ma Mingrui³,Feng Peng¹^ORCID,Xiao Hualiang⁴,Mercaldo Francesco⁵^ORCID,Santone Antonella⁵^ORCID,Qin Jing⁶^ORCID

Affiliation:

1. Key Laboratory of Optoelectronic Technology and Systems (Ministry of Education), College of Optoelectronic Engineering, Chongqing University, Chongqing, China

2. Center of Digital Dentistry, Faculty of Prosthodontics, Peking University School and Hospital of Stomatology, Beijing, China

3. College of Information Science and Engineering, Xinjiang University, Ürümqi, China

4. Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China

5. Department of Medicine and Health Sciences “Vincenzo Tiberio,”, University of Molise, Campobasso, Italy

6. School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Innovation and Technology Fund under Hong Kong Innovation and Technology Commission

Chongqing Basic Research and Frontier Exploration Project

Scientific and Technological Research Program of Chongqing Municipal Education Commission

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10003062/09868801.pdf?arnumber=9868801

Reference53 articles.

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4. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification

5. Improving accuracy in astrocytomas grading by integrating a robust least squares mapping driven support vector machine classifier into a two level grade classification scheme

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