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A hybrid global-local representation CNN model for automatic cataract grading
IEEE Journal of Biomedical and Health Informatics ( IF 6.7 ) Pub Date : 2020-02-01 , DOI: 10.1109/jbhi.2019.2914690
Xi Xu , Linglin Zhang , Jianqiang Li , Yu Guan , Li Zhang

Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for the widespread use of cataract detection to prevent blindness. Artificial intelligence assisted diagnosis based on medical images has attracted more and more attention of researchers. Many studies have focused on the use of pre-defined feature sets for cataract classification, but the predefined feature sets may be incomplete or redundant. On account of the aforementioned issues, some studies have proposed deep learning methods to automatically extract image features, but all based on global features and none has analyzed the layer-by-layer transformation process of the middle-tier features. This paper uses convolutional neural networks (CNN) to learn useful features directly from input data, and deconvolution network method is employed to investigate how CNN characterizes cataract layer-by-layer. We found that compared to the global feature set, the detail vascular information, which is lost after multi-layer convolution calculation also plays an important role in cataract grading task. And this finding fits with the morphological definition of fundus image. Through the finding, we gained insights into the design of hybrid global-local feature representation model to improve the recognition performance of automatic cataract grading.

中文翻译:

自动白内障分级的混合全局-局部表示CNN模型

白内障是导致失明的最严重的眼病之一。早期发现和治疗可以减少白内障患者的失明率。然而,眼科医师的专业知识对于临床白内障检测是必需的。因此,潜在的成本可能使得难以广泛使用白内障检测以防止失明。基于医学图像的人工智能辅助诊断越来越受到研究人员的关注。许多研究集中于将预定义特征集用于白内障分类,但是预定义特征集可能不完整或多余。鉴于上述问题,一些研究提出了深度学习方法来自动提取图像特征,但是它们都基于全局功能,没有一个人分析过中间层功能的逐层转换过程。本文使用卷积神经网络(CNN)直接从输入数据中学习有用的功能,然后使用反卷积网络方法研究CNN如何逐层表征白内障。我们发现,与全局特征集相比,多层卷积计算后丢失的详细血管信息在白内障分级任务中也起着重要作用。这一发现符合眼底图像的形态学定义。通过这一发现,我们对混合全局-局部特征表示模型的设计有了深刻的了解,以提高自动白内障分级的识别性能。本文使用卷积神经网络(CNN)直接从输入数据中学习有用的功能,然后使用反卷积网络方法研究CNN如何逐层表征白内障。我们发现,与全局特征集相比,多层卷积计算后丢失的详细血管信息在白内障分级任务中也起着重要作用。这一发现符合眼底图像的形态学定义。通过这一发现,我们对混合全局-局部特征表示模型的设计有了深刻的了解,以提高自动白内障分级的识别性能。本文使用卷积神经网络(CNN)直接从输入数据中学习有用的功能,然后使用反卷积网络方法研究CNN如何逐层表征白内障。我们发现,与全局特征集相比,多层卷积计算后丢失的详细血管信息在白内障分级任务中也起着重要作用。这一发现符合眼底图像的形态学定义。通过发现,我们对混合全局-局部特征表示模型的设计有了深刻的了解,以提高自动白内障分级的识别性能。我们发现,与全局特征集相比,多层卷积计算后丢失的详细血管信息在白内障分级任务中也起着重要作用。这一发现符合眼底图像的形态学定义。通过这一发现,我们对混合全局-局部特征表示模型的设计有了深刻的了解,以提高自动白内障分级的识别性能。我们发现,与全局特征集相比,多层卷积计算后丢失的详细血管信息在白内障分级任务中也起着重要作用。这一发现符合眼底图像的形态学定义。通过这一发现,我们对混合全局-局部特征表示模型的设计有了深刻的了解,以提高自动白内障分级的识别性能。
更新日期:2020-02-01
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