.png){kind=icon}
返回
Curriculum classification network based on margin balancing multi-loss and ensemble learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.future.2023.03.013.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a curriculum classification network based on margin-balancing multi-loss and ensemble learning method, which solves the problems of difficult classification in different age groups and uneven distance within ages groups on the basis of the orthopantomograms. Firstly, due to the characteristics of teeth development, a curriculum learning framework is proposed and divides the sample easily into high-age and low-age groups, which reduces the error of dividing low-age into high-age groups or vice versa. Secondly, owing to minor differences of teeth development in the high-age group, the margin-balancing multi-loss is proposed to optimize the classification interface. Thirdly, considering the distinguishable characteristics in the low-age group and large range and non-concentrated features, an ensemble feature learning method is proposed to improve the classification reliability. Extensive experimental results on the orthopantomograms dataset demonstrate that the proposed method can perform well on different networks and accomplish precise age estimation.(c) 2023 Elsevier B.V. All rights reserved.
Keyword:
Curriculum learning
Ensemble learning
Metric learning
Feature embedding
Margin-balancing loss
Age estimation
Medical image recognition
期刊
F
IF:
6.1
论文数:
6.9K
被引数:
2.3W
机构
引用论文
.png){kind=icon}
分享收藏分享收藏
分享收藏
查看更多内容