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- 저자 : Pang Xuejiao (검색결과 2 건)
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Pang Xuejiao,Zhao Zijian,Wu Yanbing,Chen Yong,Liu Jin 한국CDE학회 2023 Journal of computational design and engineering Vol.10 No.1
For endoscopists, large-scale screening of gastrointestinal (GI) diseases is arduous and time-consuming. While their workload and human factor-induced errors can be reduced by computer-aided diagnosis (CAD) systems, the existing ones mainly focus on a limited number of lesions or specific organs, making them unsuitable for diagnosing various GI diseases in large-scale disease screening. This paper proposes a transformer and convolutional neural network-based CAD system (called TransMSF) to assist endoscopists in diagnosing multiple GI diseases. This system constructs two feature extraction paths with different coding methods to obtain the lesions’ global and local information. In addition, downsampling is implemented in transformer to get global information of different scales, further enriching the feature representation and reducing the amount of computation and memory occupation. Moreover, a channel and spatial attention module with fewer parameters was successfully designed to pay more attention to the target and reduce the loss of important information during spatial dimension transformation. Finally, the extracted feature information is fused through the feature fusion module and then input into the linear classifier for disease diagnosis. The proposed system outperformed that of other state-of-the-art models on two datasets, reaching a 98.41% precision, a 98.15% recall, a 98.13% accuracy, and a 98.28% F1 score on the in-house GI dataset versus a 95.88% precision, a 95.88% recall, a 98.97% accuracy, and a 95.88% F1 score on the public Kvasir dataset. Moreover, TransMSF’s performance was superior to that of seasoned endoscopists. The above results prove that the proposed system is instrumental in diagnosing GI diseases in large-scale disease screening. It can also be used as a training tool for junior endoscopists to improve their professional skills by rendering helpful suggestions.
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Wu Yanbing,Zhao Zijian,Pang Xuejiao,Liu Jin 한국CDE학회 2022 Journal of computational design and engineering Vol.9 No.4
A deep learning screening model of esophageal endoscopic images can reduce the burden on endoscopists. However, most deep learning methods require many labeled data with balanced categories, and their ability to deal with new diseases not appearing in the training set is limited. This study elaborated a semi-supervised anomaly detection model for the initial screening of esophageal endoscopic images, requiring a single class of samples as a training set. The reconstruction-based method was used for anomaly detection. The model’s framework was a variational auto-encoder, with two memory modules added in latent space to restrain its generalization ability. In the memory module, a clustering operation was introduced to provide a better distribution of memory vectors, promoting their compactness with encoded features and separation from each other. A detailed description and theoretical substantiation of the proposed model were presented. A dataset containing 7989 esophageal endoscopic images labeled by experienced endoscopists was used for numerical experiments. The proposed model results were compared with those of other auto-encoder-based anomaly detection methods, outperforming them and achieving an area under the curve of 0.8212. The ablation study was also conducted to validate the effectiveness of each model’s part, and new data were successfully incorporated to assess the model feasibility and applicability range.
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