Dendritic Learning-Based Feature Fusion for Deep Networks

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

研究成果: ジャーナルへの寄稿学術論文査読

抄録

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.

本文言語英語
ページ(範囲)1554-1557
ページ数4
ジャーナルIEICE Transactions on Information and Systems
E107.D
12
DOI
出版ステータス出版済み - 2024/12

ASJC Scopus 主題領域

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能

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