Research talk: Causality for medical image analysis

Опубликовано 8 февраля 2022, 17:16

Speaker: Daniel Coelho de Castro, Senior Researcher, Microsoft Research Cambridge

Machine learning has huge potential to augment medical image analysis workflows and improve patient care. However, two of its notorious real-world challenges are the difficulty in acquiring sufficient, high-quality annotated data and mismatches between the development dataset and the target environment (across hospitals, for example).

Daniel Coelho de Castro, a researcher in the Health Intelligence Group at Microsoft Research Cambridge, will discuss how causal reasoning can shed new light on these pervasive issues and appropriate mitigation strategies. In particular, a causal perspective enables decisions about data collection, annotation, pre-processing, and learning strategies to be made—and scrutinized—more transparently. He will highlight how understanding and communicating the story behind the data helps improve the reliability of machine learning systems in high-risk healthcare settings. This session will cover a causal categorization of potential biases when developing medical imaging models, a couple of worked clinical examples, and step-by-step recommendations for practitioners.

Learn more about the 2021 Microsoft Research Summit: Aka.ms/researchsummit