Flexible Machine Learning Approaches for Computer-Assisted Surgery

Explore a 50-minute conference talk by Tom Vercauteren from King's College London on flexible machine learning approaches for computer-assisted surgery. Delve into the challenges of applying data-driven computational methods to surgical and interventional imaging fields, where heterogeneity and complex clinical workflows pose unique obstacles. Learn about ongoing interdisciplinary efforts to develop novel machine learning strategies that support, augment, and integrate into surgical workflows while providing the flexibility required for patient-specific management. Gain insights into how these approaches aim to transform the interpretation and exploitation of medical images in clinical practice, particularly within interventional suites and operating theatres. Presented as part of the Deep Learning and Medical Applications 2020 series at the Institute for Pure and Applied Mathematics, UCLA.