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Dear Deep Learning community,

We have the pleasure to present you another online meetup in our series: this time about semantic video segmentation.

Our Agenda for this meetup:
18:30 - 18:40 Welcome & Announcements
18:40 - 19:25 Main Talk: "Practical experiences in accurate video segmentation"
19:25 - 19:45 Q&A
19:45 - 20:00 Latest news & Hot topics in Deep Learning
20:00 - ~20:30 Networking: individual discussions in breakout rooms

Talk: "Practical experiences in accurate video segmentation"
by Antonis Makropoulos, Head of Machine Learning at contextflow

Semantic segmentation in the deep learning era has seen huge advances and applications in a multitude of domains. However, segmentation, and especially video segmentation, still presents significant challenges when aiming for highly accurate results required in critical applications.

In this talk Antonis will present some of the challenges they faced at ThinkSono in their journey to achieving accurate segmentation of ultrasound videos. In particular, he will discuss practical applications and shortcomings of popular methods for object detection and segmentation, as well as different techniques for video segmentation from 2D and 3D segmentation to tracking-based segmentation.

About the Speaker:
Antonis Makropoulos is a researcher on medical image analysis with machine learning. He obtained his PhD from Imperial College London which was followed by a postdoc. He then moved to the industry world, initially as a researcher for Siemens Molecular Imaging and then as the Chief Scientist of ThinkSono. He is currently Head of Machine Learning at contextflow.

As usually, we'll have room for Q&A with the speaker.
We will also bring back our regular "Hot Topics" section in this meetup, featuring some latest new papers from NeurIPS 2020 conference taking place this week.

Afterwards we'll have time for networking amongst the community in breakout groups. Have your snacks & drinks ready ;-)

We hope you can join us for this session!

Looking forward,
René, Jan, Alex, Tom

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