Medical Image Analysis with Data-efficient Learning

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• Lequan Yu (Stanford University)
• Tuesday 02 February 2021, 16:00-17:00
• https://bham-ac-uk.zoom.us/j/3900445385?pwd=Mk1LSmRiUk5STEJMWVl0bE82NTM1QT09.

If you have a question about this talk, please contact Hong Duong.

Medical imaging is a critical step in modern healthcare procedures. Accurate interpretation of medical images, e.g., CT and MRI , plays an essential role in computer-aided diagnosis, assessment, and therapy. While deep neural networks have made great progress in various medical image analysis applications, the success is largely attributed to the massive datasets with abundant annotations. However, collecting and labeling such large-scaled dataset is prohibitively expensive and time-consuming. In this talk, I will present our recent works on building data-efficient learning systems for medical image analysis, such as semi-supervised learning, multi-modality learning, and multi-site learning. The up-to-date progress and promising future directions will also be discussed.

This talk is part of the Data Science and Computational Statistics Seminar series.

This talk is included in these lists:

• Data Science and Computational Statistics Seminar
• School of Mathematics events
• https://bham-ac-uk.zoom.us/j/3900445385?pwd=Mk1LSmRiUk5STEJMWVl0bE82NTM1QT09

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