Deep Ensemble Feature Network for Gastric Section Classification

Ting Hsuan Lin, Jyun Yao Jhang, Chun Rong Huang, Yu Ching Tsai, Hsiu Chi Cheng, Bor Shyang Sheu

研究成果: Article同行評審

12 引文 斯高帕斯(Scopus)


In this paper, we propose a novel deep ensemble feature (DEF) network to classify gastric sections from endoscopic images. Different from recent deep ensemble learning methods, which need to train deep features and classifiers individually to obtain fused classification results, the proposed method can simultaneously learn the deep ensemble feature from arbitrary number of convolutional neural networks (CNNs) and the decision classifier in an end-to-end trainable manner. It comprises two sub networks, the ensemble feature network and the decision network. The former sub network learns the deep ensemble feature from multiple CNNs to represent endoscopic images. The latter sub network learns to obtain the classification labels by using the deep ensemble feature. Both sub networks are optimized based on the proposed ensemble feature loss and the decision loss which guide the learning of deep features and decisions. As shown in the experimental results, the proposed method outperforms the state-of-the-art deep learning, ensemble learning, and deep ensemble learning methods.

頁(從 - 到)77-87
期刊IEEE Journal of Biomedical and Health Informatics
出版狀態Published - 2021 1月

All Science Journal Classification (ASJC) codes

  • 生物技術
  • 電腦科學應用
  • 電氣與電子工程
  • 健康資訊管理


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