An Image Recognition Method for Urine Sediment Based on Semi-supervised Learning

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-04-01 DOI:10.1016/j.irbm.2022.09.006
Q. Ji , Y. Jiang , Z. Wu , Q. Liu , L. Qu
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引用次数: 2

Abstract

Objectives

Because there are many categories, large morphological differences and few labels of urinary sediment components, and uneven data in urine sediment images recognition, the accuracy and recall rate of the existing urine sediment images recognition methods are not ideal. The main purpose of this paper is to improve the accuracy and recall of urine sediment image recognition by proposing a urine sediment image classification method based on semi-supervised learning.

Methods

This paper proposes a method based on semi-supervised learning to classify urine sediment images. This algorithm designs a re-parameterization network (US-RepNet) for low-resolution urine sediment microscopic images to extract complex features of urine sediment images. The dual attention module is introduced on Us-RepNet to increase the extraction of fine-grained features from urine sediment images. And the cross-entropy loss (C.E. loss) function is optimized to train an unbiased classifier to improve long-tailed distribution image classification.

Results

The experimental results show that the accuracy of proposed method can reach 94% with only a small amount of labeled data for 16 types of urine sediment images under long-tail distribution.

Conclusion

The algorithm can recognize most types, and reduces the need for labeled information, while achieving excellent recognition and classification performance. Comprehensive analysis shows that it can be used as a practical reference for urine sediment analysis.

Abstract Image

基于半监督学习的尿液沉积物图像识别方法
目的由于尿沉渣成分分类多、形态差异大、标签少,以及尿沉渣图像识别数据不均匀,现有尿沉渣图像的识别方法的准确率和召回率都不理想。本文的主要目的是通过提出一种基于半监督学习的尿沉渣图像分类方法,提高尿沉渣图像识别的准确性和召回率。方法提出一种基于半监督学习的尿沉渣图像分类方法。该算法为低分辨率尿沉渣显微图像设计了一个重新参数化网络(US RepNet),以提取尿沉渣图像的复杂特征。在Us RepNet上引入了双注意力模块,以增加对尿液沉积物图像中细粒度特征的提取。并对交叉熵损失(C.E.损失)函数进行了优化,以训练一个无偏分类器来改进长尾分布图像的分类。结果实验结果表明,对于长尾分布下的16种类型的尿沉渣图像,该方法仅需少量标记数据,准确率即可达到94%。结论该算法能够识别大多数类型,减少了对标记信息的需求,同时实现了良好的识别和分类性能。综合分析表明,该方法可作为尿沉渣分析的实用参考。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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