Self-supervised learning for early detection of neurodegenerative diseases with small data - Jiang, Hongchao (Nanyang Technological University, 2023)
Neurodegenerative diseases are one of the leading causes of disability in the world. They are chronic diseases where patients experience irreversible depletion of neurons in the brain. The most common neurodegenerative diseases are Alzheimer’s disease (AD) and Parkinson’s disease (PD), where patients suffer from cognitive and motor deficiencies respectively. There is currently no cure available, and the condition progressively deteriorates affecting activities of daily living. Therefore, early intervention is crucial to alleviating symptoms and improving quality of life. Automated diagnosis tools have emerged as a viable option to diagnose patients more frequently and objectively. However, data acquisition is particularly challenging due to reasons like the rareness of disease or privacy legislation. Training a model from scratch on small data is prone to overfitting on trivial patterns.