Dendritic Learning-Based Feature Fusion for Deep Networks

Yaotong Song, Zhipeng Liu, Zhiming Zhang, Jun Tang, Zhenyu Lei, Shangce Gao

Research output: Contribution to journalArticlepeer-review

Abstract

Deep networks are undergoing rapid development. However, as the depth of networks increases, the issue of how to fuse features from different layers becomes increasingly prominent. To address this challenge, we creatively propose a cross-layer feature fusion module based on neural dendrites, termed dendritic learning-based feature fusion (DFF). Compared to other fusion methods, DFF demonstrates superior biological interpretability due to the nonlinear capabilities of dendritic neurons. By integrating the classic ResNet architecture with DFF, we devise the ResNeFt. Benefiting from the unique structure and nonlinear processing capabilities of dendritic neurons, the fused features of ResNeFt exhibit enhanced representational power. Its effectiveness and superiority have been validated on multiple medical datasets.

Original languageEnglish
Pages (from-to)1554-1557
Number of pages4
JournalIEICE Transactions on Information and Systems
VolumeE107.D
Issue number12
DOIs
StatePublished - 2024/12

Keywords

  • convolutional network
  • dendritic neuron
  • feature fusion
  • neural networks

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Artificial Intelligence

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