A Weakly Supervised Learning Method for Recognizing Childhood Tic Disorders

被引:0
作者
Zhang, Ruizhe [1 ,2 ]
Xu, Xiaojing [3 ]
Bo, Zihao [1 ,2 ]
Lyu, Junfeng [1 ,2 ]
Guo, Yuchen
Xu, Feng [1 ,2 ]
机构
[1] Tsinghua Univ, Sch Software, Beijing, Peoples R China
[2] Tsinghua Univ, BNRist, Beijing, Peoples R China
[3] China Japan Friendship Hosp, Beijing, Peoples R China
来源
ARTIFICIAL INTELLIGENCE, CICAI 2023, PT II | 2024年 / 14474卷
关键词
tic disorders; facial data processing; weakly supervised learning;
D O I
10.1007/978-981-99-9119-8_10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
So far, the number of individuals with Tic disorder worldwide has reached 59 million, and the prevalence of the disorder is rapidly increasing globally. In this work, we focus on weakly supervised learning methods for recognizing childhood tic disorders. In situations with limited data availability, we design a relative probability metric based on the characteristics of the data and a multi-phase learning algorithm is proposed based on relative probability in order to efficiently utilize coarse-labeled data in a "from easy to difficult" manner. Furthermore, the effectiveness of our method is validated through ablation experiments. Through extensive experiments on the test dataset, we demonstrate that our method behaves extraordinarily compared to baseline approaches, improving AUC by 3.0%, and facilitating expedited diagnostic assessment for medical practitioners.
引用
收藏
页码:100 / 112
页数:13
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