Machine Learning in Neuroimaging across Disciplines

14:15 – 15:05: Keynote Speech - Professor Gavin Winston

Machine Learning in Neuroimaging across Disciplines

Synopsis

Neuroimaging encompasses a range of technologies, such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), which are vital in various areas of medical practice, including diagnosis, prognosis, and treatment planning. With advances in MRI technology and increased accessibility, the volume of studies and data generated continues to rise. Machine learning methods, particularly deep learning techniques like convolutional neural networks (CNNs), are well-suited to handle the large-scale, 3D neuroimaging data.

In this talk, I will explore different machine learning approaches and their application to neuroimaging data analysis. Through examples from various neurological disorders, with a focus on MRI, I will highlight how effective machine learning integration requires close collaboration between clinicians (e.g., physicians, specialist nurses) and technical experts (e.g., engineers, computer scientists). Additionally, I will address the scientific and legal challenges in applying machine learning to neuroimaging data and discuss potential solutions moving forward.