GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status-Reference-Cited by-全球学者库

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status

Published:2022 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Zhao Wei¹^ORCID,Chen Weidao²,Li Ge³,Lei Du⁴,Yang Jiancheng⁵^ORCID,Chen Yanjing¹^ORCID,Jiang Yingjia¹,Wu Jiangfen²^ORCID,Ni Bingbing⁵^ORCID,Sun Yeqi⁶,Wang Shaokang²,Sun Yingli⁷,Li Ming⁷,Liu Jun¹^ORCID

Affiliation:

1. Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, China

2. International Center, InferVision, China

3. Department of Radiology, Xiangya Hospital, Central South University, Changsha, China

4. Department of Psychiatry and Behavioral Neuroscience, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

5. Department of Electronic Engineering and the SJTU-UCLA Joint Center for Machine Perception and Inference, Shanghai Jiao Tong University, Shanghai, China

6. Department of Pathology, The Second Xiangya Hospital, Central South University, Changsha, China

7. Department of Radiology, Huadong Hospital Affiliated to Fudan University, Shanghai, China

Funder

National Natural Science Foundation of China

Hunan Provincial Natural Science Foundation for Excellent Young Scholars

Natural Science Foundation of Hunan Province

Research Project of Postgraduate Education and Teaching Reform of Central South University

Clinical Research Center For Medical Imaging in Hunan Province

Science and Technology Innovation Program of Hunan Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09836969.pdf?arnumber=9836969

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