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Learning clinically useful information from medical images

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If you have a question about this talk, please contact Rachel Furner.

This talk will focus on the convergence medical imaging and machine learning techniques for the discovery and quantification of clinically useful information from medical images. The first part of the talk will describe machine learning techniques that can be used for image reconstruction, e.g. the acceleration of MR imaging, and image super-resolution. The second part describes recent machine learning approaches such as deep learning for image segmentation and image classification with particular application to neurological and cardiovascular diseases as well as applications in neonatal and fetal imaging.

This talk is part of the CMIH seminar series series.

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