Haotian Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Haotian Li has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Runhong Yao
Faculty of Health Sciences, Nihon Institute of Medical Science, Irumagun
1 shared publication - Takashi Kuremoto
Nippon Institute of Technology
1 shared publication - Tong Liang
Graduate School of Health Data Science, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
1 shared publication
Latest Publications
- Open Access
- 5 November 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (21) , 11812
Parkinson’s Disease Classification Using Gray Matter MRI and Deep Learning: A Comparative Framework
In this study, we propose multiple deep learning models for classifying gray matter MRI images of healthy individuals, prodromal Parkinson’s disease (PD) subjects, and diagnosed PD patients. The two proposed models extend conventional deep learning architectures—MedicalNet3D and 3D ResNet18—by performing feature extraction separately for each class and inputting these features into distinct multilayer perceptron (MLP) classifiers constructed via fine-tuning. To mitigate overfitting problem and improve generalizability, we introduce a training method based on group-wise feature fusion, in which subject IDs are separated to avoid data leakage during training. Through comparative experiments using the PPMI database, the effectiveness of the proposed approach was validated.
- 5 November 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (21) , 11812
Open AccessParkinson’s Disease Classification Using Gray Matter MRI and Deep Learning: A Comparative Framework
In this study, we propose multiple deep learning models for classifying gray matter MRI images of healthy individuals, prodromal Parkinson’s disease (PD) subjects, and diagnosed PD patients. The two proposed models extend conventional deep learning architectures—MedicalNet3D and 3D ResNet18—by performing feature extraction separately for each class and inputting these features into distinct multilayer perceptron (MLP) classifiers constructed via fine-tuning. To mitigate overfitting problem and improve generalizability, we introduce a training method based on group-wise feature fusion, in which subject IDs are separated to avoid data leakage during training. Through comparative experiments using the PPMI database, the effectiveness of the proposed approach was validated.