Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Electrical Engineering, Tunghai University, Taichung, Taiwan, China.
Comput Med Imaging Graph. 2020 Sep;84:101763. doi: 10.1016/j.compmedimag.2020.101763. Epub 2020 Jul 26.
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder.
传统的结肠镜图像计算机辅助检测系统 (CAD) 利用形状、纹理或时间信息来检测息肉，因此它们的敏感性和特异性有限。本研究提出了一种使用卷积神经网络 (CNN) 自动提取可能的息肉特征的方法。本工作的目的旨在建立一个轻量级的双编码器-解码器模型结构，用于结肠镜图像中的息肉检测。虽然该模型的结构相对较浅，但预计具有与具有更深结构的方法相似的性能。所提出的 CAD 模型由两个连续的编码器-解码器网络组成，这些网络由几个 CNN 层和全连接层组成。该模型的前端是一个异联想（也称为异编码器），它使用反向传播学习生成一组具有一定相似性的可靠损坏的标记图像，这消除了对大量训练数据的需求，而这些数据通常是医疗图像任务所必需的。这种双 CNN 架构生成一组与标记数据相似的噪声图像，以训练其对应物，自联想（也称为自编码器），以提高后继者在分类中的判别能力。自编码器还配备了 CNN，以同时捕获包含噪声的标记图像的特征。该方法使用从开放医疗数据集和浙江大学（ZJU）数据集中学到的特征，其中包含大约一千张图像。所提出的架构的性能是根据 Jaccard 指数、DICE 相似性得分和另外两个几何度量的指标与最先进的检测模型进行比较的。所提出模型性能的提高归因于自编码器中假阳性的有效减少和异联想器生成噪声候选图像。
