Machine learning in high-stake medical imaging tasks

Priberam Labs

Instituto Superior Técnico, Anfiteatro PA2

Abstract:Medical imaging is a pillar of healthcare supporting screening, diagnosis, treatment planning and assessment, as well as follow-up. However, due to changes in acquisition systems, data shortage caused by privacy concerns, among other, developed machine learning models often lack generalizability. In this talk we will see how the invariance property of causal structures may be used to improve generalizability, covering application in tabular data obtained from images, deep learning approaches and the use of generative AI to promote such several approaches may be used for tabular data.Bio:João Santinha, PhD, is a researcher specializing in medical imaging, biomarkers, and artificial intelligence. He co-leads the Digital Surgery Lab. Dr. Santinha completed his PhD at Instituto de Telecomunicações – Instituto Superior Técnico, University of Lisbon. He is also an Invited Assistant Professor at the Faculty of Medicine of the University of Lisbon. His work focuses on developing AI-driven tools to enhance surgical precision and personalize screening, diagnosis, treatments and follow-up through imaging and non-imaging data.<a href="https://www.priberam.com/" title="https://www.priberam.com/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">www.priberam.com</a>

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Machine learning in high-stake medical imaging tasks

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Machine learning in high-stake medical imaging tasks

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