Curriculum Based Multi-Task Learning for Parkinson's Disease Detection

• URL: http://arxiv.org/abs/2302.13631v1
• Date: Mon, 27 Feb 2023 09:58:09 GMT
• Title: Curriculum Based Multi-Task Learning for Parkinson's Disease Detection
• Authors: Nikhil J. Dhinagar, Conor Owens-Walton, Emily Laltoo, Christina P. Boyle, Yao-Liang Chen, Philip Cook, Corey McMillan, Chih-Chien Tsai, J-J Wang, Yih-Ru Wu, Ysbrand van der Werf, Paul M. Thompson
• Abstract summary: We leverage severity-based meta-data on the stages of disease to define a curriculum for training a deep convolutional neural network (CNN) curriculum learning is a training strategy that aims to boost classifier performance by starting with examples that are easier to classify. 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.
• Score: 2.1187904593676845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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