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首页> 外文期刊>Journal of the American Medical Informatics Association : >CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images
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CAESNet: Convolutional AutoEncoder based Semi-supervised Network for improving multiclass classification of endomicroscopic images

机译：CAESNET：基于卷积的AutoEncoder基于半监督网络，用于改善内瘤图像的多级分类

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摘要

Objective: This article presents a novel method of semisupervised learning using convolutional autoencoders for optical endomicroscopic images. Optical endomicroscopy (OE) is a newly emerged biomedical imaging modality that can support real-time clinical decisions for the grade of dysplasia. To enable real-time decision making, computer-aided diagnosis (CAD) is essential for its high speed and objectivity. However, traditional supervised CAD requires a large amount of training data. Compared with the limited number of labeled images, we can collect a larger number of unlabeled images. To utilize these unlabeled images, we have developed a Convolutional AutoEncoder based Semi-supervised Network (CAESNet) for improving the classification performance. Materials and Methods: We applied our method to an OE dataset collected from patients undergoing endoscope-based confocal laser endomicroscopy procedures for Barrett's esophagus at Emory Hospital, which consists of 429 labeled images and 2826 unlabeled images. Our CAESNet consists of an encoder with 5 convolutional layers, a decoder with 5 transposed convolutional layers, and a classification network with 2 fully connected layers and a softmax layer. In the unsupervised stage, we first update the encoder and decoder with both labeled and unlabeled images to learn an efficient feature representation. In the supervised stage, we further update the encoder and the classification network with only labeled images for multiclass classification of the OE images. Results: Our proposed semisupervised method CAESNet achieves the best average performance for multiclass classification of OE images, which surpasses the performance of supervised methods including standard convolutional networks and convolutional autoencoder network. Conclusions: Our semisupervised CAESNet can efficiently utilize the unlabeled OE images, which improves the diagnosis and decision making for patients with Barrett's esophagus.
机译：目的：本文介绍了使用卷积自动化器进行光学元素图像的半精华学习的新方法。光学元素（OE）是一种新出现的生物医学成像模态，可以支持Dysplasia等级的实时临床决策。为了实现实时决策，计算机辅助诊断（CAD）对于其高速和客观性至关重要。但是，传统的监督CAD需要大量的培训数据。与有限数量的标记图像相比，我们可以收集更多数量的未标记图像。要利用这些未标记的图像，我们开发了一种基于卷积的AutoEncoder的半监督网络（CAESNet），用于提高分类性能。材料和方法：我们将我们的方法应用于从接受基于内窥镜的共焦激光端子复制程序的OE数据集，在埃默里医院的Barrett食管中进行的基于内窥镜的共焦激光端子显微镜程序，该方法由429个标记的图像和2826个未标记的图像组成。我们的CAESNET由具有5个卷积层的编码器组成，一个带有5个转置卷积层的解码器，以及具有2个完全连接的层和软MAX层的分类网络。在无监督阶段，我们首先使用标记和未标记的图像更新编码器和解码器来学习有效的特征表示。在监督阶段，我们进一步更新编码器和分类网络，仅用标记的图像进行OE图像的多字符分类。结果：我们提出的半质化方法CAESNET实现了OE图像的多字母分类的最佳平均性能，这些性能超越了监督方法，包括标准卷积网络和卷积AutoEncoder网络。结论：我们的半熟地CAESNET可以有效地利用未标记的OE图像，这改善了Barrett食道患者的诊断和决策。

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