Dendritic Deep Learning for Medical Segmentation

被引：0

作者：

Zhipeng Liu ^{[1
]}

Zhiming Zhang ^{[1
]}

Zhenyu Lei ^{[1
]}

Masaaki Omura ^{[1
]}

Rong-Long Wang ^{[2
]}

Shangce Gao ^{[3
,1
]}

机构：

[1] the Faculty of Engineering, University of Toyama

[2] the Faculty of Engineering, University of Fukui

[3] IEEE

来源：

IEEE/CAA Journal of Automatica Sinica | 2024年 / 11卷 / 03期

基金：

日本科学技术振兴机构; 日本学术振兴会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论]; TP391.41 []; R319 [其他科学技术在医学上的应用];

学科分类号：

080203 ; 081104 ; 0812 ; 0835 ; 1001 ; 1405 ;

摘要：

Dear Editor,This letter presents a novel segmentation approach that leverages dendritic neurons to tackle the challenges of medical imaging segmentation. In this study, we enhance the segmentation accuracy based on a SegNet variant including an encoder-decoder structure, an upsampling index, and a deep supervision method. Furthermore, we introduce a dendritic neuron-based convolutional block to enable nonlinear feature mapping, thereby further improving the effectiveness of our approach.

引用

页码：803 / 805

页数：3

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