TY - GEN
T1 - Modeling Parkinson's Disease Aided Diagnosis with Multi-Instance Learning
T2 - 29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023
AU - Xu, Zheyuan
AU - Nan, Fengtao
AU - Qi, Jun
AU - Yang, Yun
AU - Wang, Xulong
AU - Yang, Po
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - An effective auxiliary diagnostic model for the severity of Parkinson's disease (PD) could help hospitals reduce their workload, particularly in nations or regions where medical resources are limited. However, a critical challenge persists that hampers the progress of such endeavors. Previous studies have employed label propagation techniques that assign uniform labels to all activity signal segments of a patient, neglecting the complex expression of PD symptoms, thereby introducing label noise. To confront this challenge, we have collected an extensive set of PD activity signals from a clinical setting and have proposed an efficient and robust framework for assessing PD severity. Specifically, we gathered wearable device data on 14 daily activities from 70 PD patients, based on the Unified Parkinson's Disease Rating Scale Part III. Our data analysis indicates that many segments within the activities were incorrectly labeled, significantly impairing the classification performance of the model. We introduced a novel framework based on Multi-Instance Learning with a Re-weighted Discriminative Instance Mapping (RDIM) to model PD auxiliary diagnosis, aiming to eliminate the impact of label noise present in the data. The results demonstrate that our framework achieves an accuracy of 80.88% in classifying the severity of PD, effectively addressing the label noise caused by coarse-grained label propagation.
AB - An effective auxiliary diagnostic model for the severity of Parkinson's disease (PD) could help hospitals reduce their workload, particularly in nations or regions where medical resources are limited. However, a critical challenge persists that hampers the progress of such endeavors. Previous studies have employed label propagation techniques that assign uniform labels to all activity signal segments of a patient, neglecting the complex expression of PD symptoms, thereby introducing label noise. To confront this challenge, we have collected an extensive set of PD activity signals from a clinical setting and have proposed an efficient and robust framework for assessing PD severity. Specifically, we gathered wearable device data on 14 daily activities from 70 PD patients, based on the Unified Parkinson's Disease Rating Scale Part III. Our data analysis indicates that many segments within the activities were incorrectly labeled, significantly impairing the classification performance of the model. We introduced a novel framework based on Multi-Instance Learning with a Re-weighted Discriminative Instance Mapping (RDIM) to model PD auxiliary diagnosis, aiming to eliminate the impact of label noise present in the data. The results demonstrate that our framework achieves an accuracy of 80.88% in classifying the severity of PD, effectively addressing the label noise caused by coarse-grained label propagation.
KW - Inexact Supervision
KW - Label Noise
KW - Multi-Instance Learning
KW - Parkinson's Disease
KW - Wearable Devices
UR - http://www.scopus.com/inward/record.url?scp=85190304920&partnerID=8YFLogxK
U2 - 10.1109/ICPADS60453.2023.00139
DO - 10.1109/ICPADS60453.2023.00139
M3 - Conference Proceeding
AN - SCOPUS:85190304920
T3 - Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS
SP - 936
EP - 943
BT - Proceedings - 2023 IEEE 29th International Conference on Parallel and Distributed Systems, ICPADS 2023
PB - IEEE Computer Society
Y2 - 17 December 2023 through 21 December 2023
ER -