Name: Machine learning for medical image analysis - How AI is changing medical imaging
Start: 2024-05-02T16:00:00+02:00
End: 2024-05-02T17:00:00+02:00

We shall talk about spotting cancer in tissue samples, predicting heart disease from ultrasound, discovering new drugs, or detecting tooth caries. I shall present several examples of how machine learning and especially deep learning methods can be applied to medical image analysis. The techniques used will range from generalized multiple instance learning, through adversarial learning (GANs), to deep object detection (e.g. YOLO).

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⌚️ **Start**: 16:00 and end at 17:00, both online and offline in Miton offices, Křížíkova 34, Praha - reception will have the necessary information.
🎙️ **Speaker:** [Jan Kybic](https://www.linkedin.com/in/jan-kybic-0b0603/)
🍻 **Networking** after the talk.
**🎥 Recording:** After the event we will publish a recording and post a link to it in the comments.
🚪**Doors open** at 15:45, and the event officially starts at 16:00.
Your expertise is about to take off. Can't wait to have you on board!

GLAMI

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Jan Kybic is a professor at the Faculty of Electrical Engineering, Czech Technical University in Prague where he leads a Biomedical Image Analysis group and the Department of Cybernetics. He obtained a Ph.D. degree in biomedical image processing from EPFL, Switzerland, in 2001 and was a post-doc researcher at INRIA, Sophia-Antipolis, France. 

He wrote over 40 scientific journal articles and over 100 articles in international conferences and his work was cited over 5000 times (according to Google Scholar). His research interests include signal and image processing for biomedical applications image registration, segmentation, splines and wavelets, numerical methods, inverse problems, algorithm theory, machine learning, and recently also more and more deep learning.

prof. Jan Kybic

Machine learning for medical image analysis - How AI is changing medical imaging

MITON CZ, s.r.o.

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Machine learning for medical image analysis - How AI is changing medical imaging

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