Improving Tumor Classification by Reusing Self-Predicted Segmentation of Medical Images as Guiding Knowledge-Reference-Cited by-同舟云学术

Improving Tumor Classification by Reusing Self-Predicted Segmentation of Medical Images as Guiding Knowledge

Published:2024-01 Issue:1 Volume:28 Page:122-133
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Lin Xiaoyi¹^ORCID,Wang Mingyu²^ORCID,Li Fei²^ORCID,Xu Ziyue³^ORCID,Chen Jia⁴,Chen Xin⁵^ORCID,Yuan Chenglang²,Wu Songxiong⁶,Luo Yanji⁷,Shen Jingxian⁸,Feng Shi-Ting⁷^ORCID,Huang Bingsheng²

Affiliation:

1. Department of Radiology, Shenzhen Second People's Hospital, Shenzhen University, Shenzhen, China

2. Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, China

3. Nvidia Corporation, Bethesda, MD, USA

4. Shenzhen Institute for Drug Control, Shenzhen University, Shenzhen, China

5. National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Medical School, Shenzhen University, Shenzhen, China

6. Radiology Department General Hospital, Shenzhen University and Shenzhen University Clinical Medical Academy, Shenzhen, China

7. Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

8. Department of Radiology, Sun Yat-sen University Cancer Center, Guangzhou, China

Funder

National Natural Science Foundation of China

Marshall Lab of Biomedical Engineering

Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10380963/10174638.pdf?arnumber=10174638

Reference40 articles.

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3. Dynamic computed tomography is useful for prediction of pathological grade in pancreatic neuroendocrine neoplasm

4. Pancreatic neuroendocrine tumour (PNET): Staging accuracy of MDCT and its diagnostic performance for the differentiation of PNET with uncommon CT findings from pancreatic adenocarcinoma

5. Prediction of Pancreatic Neuroendocrine Tumor Grade Based on CT Features and Texture Analysis

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