A multi‐classification model for non‐small cell lung cancer subtypes based on independent subtask learning-Reference-Cited by-同舟云学术

A multi‐classification model for non‐small cell lung cancer subtypes based on independent subtask learning

Published:2022-06-26 Issue:11 Volume:49 Page:6960-6974
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Medical Physics

Author:

Li Jinkai¹²,Song Fan¹,Zhang Peng¹,Ma Chenbin¹,Zhang Tianyi¹,Sun Yangyang¹,Feng Youdan¹,Song Xiao³,Lyu Shangqing⁴,Zhang Guanglei¹

Affiliation:

1. Beijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing China

2. School of General Engineering Beihang University Beijing China

3. School of Medical Imaging Shanxi Medical University Taiyuan China

4. School of Electronics & Computer Science University of Southampton Southampton UK

Funder

Natural Science Foundation of Beijing Municipality

National Natural Science Foundation of China

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15808

Reference39 articles.

1. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012

2. Cancer statistics, 2017

3. Glucose Metabolism in NSCLC Is Histology-Specific and Diverges the Prognostic Potential of 18FDG-PET for Adenocarcinoma and Squamous Cell Carcinoma

4. Differences in metabolism between adeno- and squamous cell non-small cell lung carcinomas: Spatial distribution and prognostic value of GLUT1 and MCT4

5. Radiomics: Extracting more information from medical images using advanced feature analysis

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