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Growing with the Help of Multiple Teachers: Lightweight and Noise-Resistant Student Model for Medical Image Classification

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成果类型:
会议论文
作者:
Song, Yucheng;Wang, Jincan;Ge, Yifan;Liao, Zhifang;Lan, Peng;...
通讯作者:
Liao, ZF
作者机构:
[Wang, Jincan; Song, Yucheng; Lan, Peng; Ge, Yifan; Liao, Zhifang] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China.
[Guo, Jia] Cent South Univ, Xiangya Sch Nursing, Changsha, Peoples R China.
[Li, Lifeng] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Dept Radiol, Changsha, Hunan, Peoples R China.
[Li, Lifeng] Nanchang Univ, Med Imaging Ctr, Affiliated Hosp 1, Nanchang, Jiangxi, Peoples R China.
通讯机构:
[Liao, ZF ] C
Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China.
语种:
英文
关键词:
Medical image classification;Noisy label;Knowledge distillation with multiple teachers;Deep learning;Point-of-care
期刊:
Lecture Notes in Computer Science
ISSN:
0302-9743
年:
2025
卷:
15044
页码:
194-208
会议名称:
7th Chinese Conference on Pattern Recognition and Computer Vision
会议论文集名称:
Lecture Notes in Computer Science
会议时间:
OCT 18-20, 2024
会议地点:
Urumqi, PEOPLES R CHINA
会议主办单位:
[Song, Yucheng;Wang, Jincan;Ge, Yifan;Liao, Zhifang;Lan, Peng] Cent South Univ, Sch Comp Sci & Engn, Changsha, Peoples R China.^[Guo, Jia] Cent South Univ, Xiangya Sch Nursing, Changsha, Peoples R China.^[Li, Lifeng] Univ South China, Affiliated Changsha Cent Hosp, Hengyang Med Sch, Dept Radiol, Changsha, Hunan, Peoples R China.^[Li, Lifeng] Nanchang Univ, Med Imaging Ctr, Affiliated Hosp 1, Nanchang, Jiangxi, Peoples R China.
主编:
Lin, Z Cheng, MM He, R Ubul, K Silamu, W Zha, H Zhou, J Liu, CL
出版地:
152 BEACH ROAD, #21-01/04 GATEWAY EAST, SINGAPORE, 189721, SINGAPORE
出版者:
SPRINGER-VERLAG SINGAPORE PTE LTD
ISBN:
978-981-97-8495-0; 978-981-97-8496-7
基金类别:
National Natural Science Foundation of China, Regional Science Fund Project [72264037]
机构署名:
本校为其他机构
摘要:
In recent years, the development of medical imaging technology has transformed imaging solutions from laboratory-based to point-of-care imaging with real-time capabilities. However, these point-of-care devices are often constrained by environmental factors such as ambient light and noise, leading to poor image quality and consequently affecting the diagnostic accuracy of point-of-care devices. Furthermore, due to the need for lightweight models in point-of-care devices, traditional models fail to meet requirements in terms of computational resources, model parameters, and inference time. There...

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