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Cross-Attention Based Multi-Resolution Feature Fusion Model for Self-Supervised Cervical OCT Image Classification

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成果类型:
期刊论文
作者:
Wang, Qingbin;Chen, Kaiyi;Dou, Wanrong;Ma, Yutao
通讯作者:
Ma, YT
作者机构:
[Dou, Wanrong; Wang, Qingbin; Chen, Kaiyi] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.
[Ma, Yutao] Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
[Ma, Yutao] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.
通讯机构:
[Ma, YT ] C
Cent China Normal Univ, Sch Comp Sci, Wuhan 430079, Peoples R China.
Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smart, Wuhan 430079, Peoples R China.
语种:
英文
关键词:
Cervical cancer;cross-attention;optical coherence tomography;self-supervised learning;vision transformer
期刊:
IEEE/ACM Transactions on Computational Biology and Bioinformatics
ISSN:
1545-5963
年:
2023
卷:
20
期:
4
页码:
2541-2554
基金类别:
National Key Research and Development Program of China [2020AAA0107705]; Science and Technology Major Project of Hubei Province in China (Next-Generation AI Technologies) [2019AEA170]
机构署名:
本校为通讯机构
院系归属:
计算机学院
摘要:
Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive res...

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