Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates

doi:10.1016/j.bspc.2021.103120

CIOMP OpenIR

	Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates
	H. Wei; W. Gao; H. T. Nie; J. Q. Sun and M. Zhu
	2022
发表期刊	Biomedical Signal Processing and Control
ISSN	1746-8094
卷号	71 期号:11
摘要	A fully automated chromosome analysis system can substitute cytogenetic experts for the task of chromosome karyotype analysis, which in turn can substantially increase the efficiency of disease diagnosis. However, the construction of such a system is most crucially restricted by the accuracy of chromosome classification, during karyotype analysis. To facilitate the construction of an automatic chromosome analysis system, an input-aware and probabilistic prediction convolutional neural network (IAPP-CNN) is presented in this paper for high accuracy of chromosome classification. The approach follows three stages and consists of one input-aware module, one feature extractor module and one probabilistic prediction module. In the first stage, the input-aware module develops raw images automatically into the global-scale image, the object-scale image and the part-scale image, by introducing an attention mechanism. In the second stage, the three scale images are input into the feature extraction module through three branches, then the respective feature operators are obtained via their independent CNN feature extractors. In the third stage, the probabilistic prediction module uses three dynamic probabilistic parameters to estimate the prediction of each CNN branch separately, and then combined the three CNN votes for the final decision. The feature expression ability of the key feature was improved and the network was enabled to focus on the recognizable regions in the image. Evaluation results from a large dataset of healthy patients showed that the proposed IAPP-CNN achieved the highest accuracy of 99.2% for the chromosome classification task, surpassing the performance of a competitive baseline created by state-of-the-art methods.
DOI	10.1016/j.bspc.2021.103120
URL	查看原文
收录类别	SCI
引用统计
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/65094
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	H. Wei,W. Gao,H. T. Nie,et al. Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates[J]. Biomedical Signal Processing and Control,2022,71(11).
APA	H. Wei,W. Gao,H. T. Nie,&J. Q. Sun and M. Zhu.(2022).Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates.Biomedical Signal Processing and Control,71(11).
MLA	H. Wei,et al."Classification of Giemsa staining chromosome using input-aware deep convolutional neural network with integrated uncertainty estimates".Biomedical Signal Processing and Control 71.11(2022).

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Classification of Gi（4564KB）	期刊论文	出版稿	开放获取	CC BY-NC-SA	浏览下载