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Authors: Arunava Chaudhuri ; Abhishek Sambyal and Deepti Bathula

Affiliation: Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, 140001, Punjab, India

Keyword(s): Parkinson’s Disease, Multi-Modal, Classification, Siamese Network, Deep Learning.

Abstract: Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that affects the central nervous system and causes both motor and non-motor symptoms. While movement related symptoms are the most noticeable early signs, others like loss of smell can occur quite early and are easy to miss. This suggests that multi-modal assessment has significant potential in early diagnosis of PD. Multi-modal analysis allows for synergistic fusion of complementary information for improved prediction accuracy. However, acquiring all modalities for all subjects is not only expensive but also impractical in some cases. This work attempts to address the missing modality problem where the data is mutually exclusive. Specifically, we propose to leverage two distinct and unpaired datasets to improve the classification accuracy of PD. We propose a two-stage strategy that combines individual modality classifiers to train a multi-modality classifier using siamese network with Triplet Loss. Furthermore, we use a Max-Voting strategy applied to Mix-and-Match pairing of the unlabelled test sample of one modality with both positive and negative samples from the other modality for test-time inference. We conducted experiments using gait sensor data (PhysioNet) and clinical data (PPMI). Our experimental results demonstrate the efficacy of the proposed approach compared to the state-of-the-art methods using single modality analysis. (More)

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Paper citation in several formats:
Chaudhuri, A.; Sambyal, A. and Bathula, D. (2024). Mutually Exclusive Multi-Modal Approach for Parkinson’s Disease Classification. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING; ISBN 978-989-758-688-0; ISSN 2184-4305, SciTePress, pages 236-243. DOI: 10.5220/0012376100003657

@conference{bioimaging24,
author={Arunava Chaudhuri. and Abhishek Sambyal. and Deepti Bathula.},
title={Mutually Exclusive Multi-Modal Approach for Parkinson’s Disease Classification},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING},
year={2024},
pages={236-243},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012376100003657},
isbn={978-989-758-688-0},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING
TI - Mutually Exclusive Multi-Modal Approach for Parkinson’s Disease Classification
SN - 978-989-758-688-0
IS - 2184-4305
AU - Chaudhuri, A.
AU - Sambyal, A.
AU - Bathula, D.
PY - 2024
SP - 236
EP - 243
DO - 10.5220/0012376100003657
PB - SciTePress

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