[1]陈少滨,雷柏英,谢海,等.基于混合注意力的早产儿视网膜病变分类方法[J].深圳大学学报理工版,2022,39(6):701-708.[doi:10.3724/SP.J.1249.2022.06701]
 CHEN Shaobin,LEI Baiying,XIE Hai,et al.Retinopathy of prematurity classification based on mixed attention[J].Journal of Shenzhen University Science and Engineering,2022,39(6):701-708.[doi:10.3724/SP.J.1249.2022.06701]
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基于混合注意力的早产儿视网膜病变分类方法()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第6期
页码:
701-708
栏目:
电子与信息科学
出版日期:
2022-11-15

文章信息/Info

Title:
Retinopathy of prematurity classification based on mixed attention
文章编号:
202206012
作者:
陈少滨雷柏英谢海张国明杜曰山一赵欣予
1)深圳大学医学部生物医学工程学院,深圳 518060;2)暨南大学附属深圳市眼科医院,深圳 518040
Author(s):
CHEN Shaobin LEI Baiying XIE Hai ZHANG Guoming DU Yueshanyi ZHAO Xinyu
1) School of Biomedical Engineering, Faculty of Medicine, Shenzhen University, Shenzhen 518060, P.R.China 2) Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen 518040, P.R.China
关键词:
人工智能早产儿视网膜病变半监督学习自注意力空间注意力通道注意力
Keywords:
artificial intelligence retinopathy of prematurity semi-supervised learning self-attention spatial attention channel attention
分类号:
TP391
DOI:
10.3724/SP.J.1249.2022.06701
文献标志码:
A
摘要:
急进型后极部早产儿视网膜病变(aggressive posterior retinopathy of prematurity, AP-ROP)的患儿若不能及时得到诊断,易失去最佳治疗时间,导致患儿视网膜完全脱离,并最终失明.为更好地从ROP病例中诊断出这种特殊类型的AP-ROP,提出一种基于混合注意力的半监督学习方法.该方法分别通过低级数据增强器和高级数据增强器获得未标记训练数据的两种不同级别的增强数据.先利用低级数据增强器获得的增强数据生成未标记数据的伪标签,所得伪标签进一步被用作通过高级数据增强器获得的增强数据的训练标签.在设计的分类器模型中,将自注意力、空间注意力和通道注意力相结合,增强了分类器的特征提取能力.实验结果表明,本研究方法可以用很少的标记数据实现能够与全监督学习相媲美的性能,从而实现早产儿视网膜病变的自动检测,进而辅助医生进行ROP和AP-ROP的早期筛查.
Abstract:
Children with aggressive posterior retinopathy of prematurity (AP-ROP) are prone to lose the best treatment time if they are not diagnosed in time, leading to complete retinal detachment and eventual blindness. In order to better diagnose this special type of AP-ROP from ROP cases, we propose a semi-supervised learning method based on mixed attention. We obtain two different data representations of unlabeled training data through a weak enhancer and a strong enhancer, respectively. First, the enhanced data obtained by the weak enhancer is used to generate a pseudo label of the unlabeled data, which will be further used as a supervised training label of the enhanced data obtained by the strong enhancer. In the devised model, we combine self-attention, spatial attention and channel attention to enhance the capabilities of feature extraction. Experimental results show that our proposed method can achieve performance comparable to fully-supervised learning with very little labeled data, thereby realizing automatic detection of retinopathy of prematurity, and assisting doctors in the early screening of ROP and AP-ROP.

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更新日期/Last Update: 2022-11-30