Research talk: Causality for medical image analysis
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: https://Aka.ms/researchsummit (opens in new tab)
- Track:
- Causal Machine Learning
- Date:
- Speakers:
- Daniel Coelho de Castro
- Affiliation:
- Microsoft Research
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Daniel Coelho de Castro
Senior Researcher
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Causal Machine Learning
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Opening remarks: Causal Machine Learning
Speakers:- Cheng Zhang
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Research talk: Causal ML and business
Speakers:- Jacob LaRiviere
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Panel: Challenges and opportunities of causality
Speakers:- Susan Athey,
- Yoshua Bengio,
- Judea Pearl
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Research talk: Causal ML and fairness
Speakers:- Allison Koenecke
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Panel: Causal ML Research at Microsoft
Speakers:- Daniel McDuff,
- Javier González,
- Justin Ding
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Research talk: Post-contextual-bandit inference
Speakers:- Nathan Kallus
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Panel: Causal ML in industry
Speakers:- Ya Xu,
- Totte Harinen,
- Dawen Liang
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