Modeling Parkinson's Disease Aided Diagnosis with Multi-Instance Learning: An Effective Approach to Mitigate Label Noise

Zheyuan Xu, Fengtao Nan, Jun Qi*, Yun Yang, Xulong Wang, Po Yang*

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 29th International Conference on Parallel and Distributed Systems, ICPADS 2023
PublisherIEEE Computer Society
Pages936-943
Number of pages8
ISBN (Electronic)9798350330717
DOIs
Publication statusPublished - 2023
Event29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023 - Ocean Flower Island, Hainan, China
Duration: 17 Dec 202321 Dec 2023

Publication series

NameProceedings of the International Conference on Parallel and Distributed Systems - ICPADS
ISSN (Print)1521-9097

Conference

Conference29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023
Country/TerritoryChina
CityOcean Flower Island, Hainan
Period17/12/2321/12/23

Keywords

  • Inexact Supervision
  • Label Noise
  • Multi-Instance Learning
  • Parkinson's Disease
  • Wearable Devices

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