Attention-Based Densely Connected Convolutional Network for Chromosome Classification-Reference-Cited by-同舟云学术

Attention-Based Densely Connected Convolutional Network for Chromosome Classification

Published:2022-03-18 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 8th International Conference on Computing and Artificial Intelligence
language:
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Author:

Gong Zhenjia¹,Peng Bo²,Shen Ao³,Gong Yan⁴,Zhang Jingzhong⁵,Lang Song⁶,Zhang Yanwei⁶,Xu Linyu⁶,Zheng Hanqing⁶,Pang Chunying⁷,Dai Yakang²

Affiliation:

1. Life Science and Technology department, Changchun University of Science and Technology Sciences, China and Medical Image Processing Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China

2. Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China and Medical Imaging Department, Jinan Guoke Medical Engineering Technology Development, China

3. Computer Science and Technology, Changchun University of Science and Technology Sciences, China and Medical Image Processing Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China

4. Medical Optics Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China and Medical Imaging Department, Jinan Guoke Medical Engineering Technology Development, China

5. Medical Diagnostics Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China

6. Medical Optics Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, China

7. Life Science and Technology department, Changchun University of Science and Technology Sciences, China

Funder

National Nature Science Foundation of China

Jiangsu Innovation Team

Major Innovative Research Team of Suzhou

Suzhou Science & Technology Projects

Jinan Innovation Team, Quancheng 5150 Project

Shandong Natural Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3532213.3532294

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