Trusted detection for Parkinson's disease based on uncertainty estimation  

作　　者：Zhou Lin Li Yun Ji Wei Liu Yuxuan Zheng Huifen 

机构地区：[1]School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China [2]School of Communications and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China [3]Bell Honors School,Nanjing University of Posts and Telecommunications,Nanjing 210023,China [4]Affiliated Geriatric Hospital of Nanjing Medical University,Nanjing 210024,China

出　　处：《The Journal of China Universities of Posts and Telecommunications》2024年第5期85-94,共10页中国邮电高校学报（英文版）

基　　金：supported by the Basic Scientific(Natural Science)Major Program of the Higher Education Institutions of Jiangsu Province,China(21KJA520003)。

摘　　要：Currently,most deep learning methods used for Parkinson's disease(PD)detection lack reliability assessment.This characteristic makes it is difficult to identify erroneous results in practice,leading to potentially serious consequences.To address this issue,a prior network with the distance measure(PNDM)layer was proposed in this paper.PNDM layer consists of two modules:prior network(PN)and the distance measure(DM)layer.The prior network is employed to estimate data uncertainty,and the DM layer is utilized to estimate model uncertainty.The goal of this work is to provide accurate and reliable PD detection through uncertainty estimation.Experiments show that PNDM layer can effectively estimate both model uncertainty and data uncertainty,rendering it more suitable for uncertainty estimation in PD detection compared to existing methods.

关 键 词：Parkinson’s disease(PD) deep learning uncertainty estimation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R742.5[自动化与计算机技术—控制科学与工程]