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

Research output: Contribution to journalArticlepeer-review

3 Citations (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.

Original languageEnglish
Article number9107452
Pages (from-to)77-87
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Issue number1
Publication statusPublished - 2021 Jan

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


Dive into the research topics of 'Deep Ensemble Feature Network for Gastric Section Classification'. Together they form a unique fingerprint.

Cite this