How can we … use machine learning to make medical imaging segmentation more accessible?
14 April 2025
Medical image segmentation is inherently time-consuming and costly due to the reliance on expert manual labelling. In this process, highly trained professionals, such as radiologists or specialized doctors, meticulously delineate structures and anomalies in imaging data. I am exploring segmentation methods with the aim of building an automatic model that can process images more quickly and efficiently while preserving the accuracy of results using machine learning. I would also like to explore the interoperability of the model since we would like to understand how the model performs. This would result in cost savings, making segmentation scans more accessible , which is good news for health services, patients, and clinicians, who could benefit from more accurate results.
