@inproceedings{2f2cbbbfcb6b4dc0ac54eab97dacc036,
title = "Curriculum Based Multi-Task Learning for Parkinson's Disease Detection",
abstract = "There is great interest in developing radiological classifiers for diagnosis, staging, and predictive modeling in progressive diseases such as Parkinson's disease (PD), a neurodegenerative disease that is difficult to detect in its early stages. Here we leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN). Typically, deep learning networks are trained by randomly selecting samples in each mini-batch. By contrast, curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. Here we define a curriculum to progressively increase the difficulty of the training data corresponding to the Hoehn and Yahr (H&Y) staging system for PD (total N=1,012; 653 PD patients, 359 controls; age range: 20.0-84.9 years). Even with our multi-task setting using pre-trained CNNs and transfer learning, PD classification based on T1-weighted (T1-w) MRI was challenging (ROC AUC: 0.59-0.65), but curriculum training boosted performance (by 3.9%) compared to our baseline model. Future work with multimodal imaging may further boost performance.",
keywords = "Parkinson's disease, curriculum learning, multi-task, staging",
author = "Dhinagar, {Nikhil J.} and Conor Owens-Walton and Emily Laltoo and Boyle, {Christina P.} and Yao-Liang Chen and Philip Cook and Corey McMillan and Chih-Chien Tsai and Wang, {J. J.} and Yih-Ru Wu and {van der Werf}, Ysbrand and Thompson, {Paul M.}",
note = "Funding Information: This work was supported by the U.S. National Institutes of Health, under NIH grant R01NS107513 and U01AG068057. Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",
year = "2023",
doi = "https://doi.org/10.1109/ISBI53787.2023.10230355",
language = "English",
volume = "2023-April",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",
address = "United States",
}