Application of deep learning to the classification of images from colposcopy.
Application of deep learning to the classification of images from colposcopy.
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DOI：
10.3892/ol.2018.7762
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发表时间：
2018-03
期刊：
影响因子：
2.9
通讯作者：
Yokota H
中科院分区：
文献类型：
--
作者：
Sato M;Horie K;Hara A;Miyamoto Y;Kurihara K;Tomio K;Yokota H
本研究的目的是探讨深度学习能否成功地应用于阴道镜图像的分类。为此，共登记了158名接受锥切术的患者，并对妇科肿瘤学数据库中的医疗记录和数据进行了回顾。使用Kera神经网络和TensorFlow文库进行深度学习。以术前阴道镜图像为输入数据，采用深度学习技术，将患者分为重度不典型增生组、原位癌(CIS)组和浸润性癌(IC)组。共获得485幅图像用于分析，其中重度不典型增生142幅(2.9幅/例)，CIS 257幅(3.3幅/例)，IC 86幅(4.1幅/例)。在这些图像中，233张是使用绿色滤镜拍摄的，其余252张是没有使用绿色滤镜拍摄的。在应用L2正则化、L1正则化、丢弃和数据增强之后，验证数据集的准确率为~50%。虽然目前的研究是初步的，但结果表明深度学习可以应用于阴道镜图像的分类。
The objective of the present study was to investigate whether deep learning could be applied successfully to the classification of images from colposcopy. For this purpose, a total of 158 patients who underwent conization were enrolled, and medical records and data from the gynecological oncology database were retrospectively reviewed. Deep learning was performed with the Keras neural network and TensorFlow libraries. Using preoperative images from colposcopy as the input data and deep learning technology, the patients were classified into three groups [severe dysplasia, carcinoma in situ (CIS) and invasive cancer (IC)]. A total of 485 images were obtained for the analysis, of which 142 images were of severe dysplasia (2.9 images/patient), 257 were of CIS (3.3 images/patient), and 86 were of IC (4.1 images/patient). Of these, 233 images were captured with a green filter, and the remaining 252 were captured without a green filter. Following the application of L2 regularization, L1 regularization, dropout and data augmentation, the accuracy of the validation dataset was ~50%. Although the present study is preliminary, the results indicated that deep learning may be applied to classify colposcopy images.
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