Archives: Thesis

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11DEC 2020
Ph.D. Gallery
Tobias Würfl
Applications of Data Consistency Conditions in Cone-Beam Computed Tomography The mathematical model of computed tomography data acquisition shows that every such measurement contains a degree of redundancy. This redundancy can be used to formulate data consistency conditions which enable quantifying how accurately a measurement fulﬁlls the mathematical model. This enables compensating for inaccuracies in the […]Applications of Data Consistency Conditions in Cone-Beam Computed Tomography The mathematical model of computed tomography data acquisition shows that every such measurement contains a degree of redundancy. This redundancy can be used to formulate data consistency conditions which enable quantifying how accurately a measurement fulﬁlls the mathematical model. This enables compensating for inaccuracies in the […]
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11DEC 2020
News
Invited Talk by Mathias Unberath, Johns Hopkins University @JHUCompSci, Mon. Dec 14th, 16h
It’s a great pleasure to welcome Prof. Dr. Mathias Unberath back to FAU. This Monday, he will present his newest works to us! Title: Bridging Domains in Medical Imaging — Differentiable Mappings Between 2- and 3-Dimensional Data DomainsDate: Monday, December 14th, 16hLocation: https://fau.zoom.us/j/98207594739?pwd=T0lSeGxPOXVQQTkrTWI3djV0eDZjdz09 Abstract: Differentiably connecting 2- and 3-dimensional domains is of substantial interest in […]It’s a great pleasure to welcome Prof. Dr. Mathias Unberath back to FAU. This Monday, he will present his newest works to us! Title: Bridging Domains in Medical Imaging — Differentiable Mappings Between 2- and 3-Dimensional Data DomainsDate: Monday, December 14th, 16hLocation: https://fau.zoom.us/j/98207594739?pwd=T0lSeGxPOXVQQTkrTWI3djV0eDZjdz09 Abstract: Differentiably connecting 2- and 3-dimensional domains is of substantial interest in […]
Read more about Invited Talk by Mathias Unberath, Johns Hopkins University @JHUCompSci, Mon. Dec 14th, 16h
11DEC 2020
News
New PhD student
Noah Maul received his Master’s degree in Computer Science from Friedrich-Alexander-Universität Erlangen-Nürnberg and joined the Pattern Recognition Lab in December 2020 as a Ph.D. candidate under the supervision of Prof. Andreas Maier. The research project focuses on learning-based approaches for the quantification of blood flow in 4D digital subtraction angiography and is carried out in […]Noah Maul received his Master’s degree in Computer Science from Friedrich-Alexander-Universität Erlangen-Nürnberg and joined the Pattern Recognition Lab in December 2020 as a Ph.D. candidate under the supervision of Prof. Andreas Maier. The research project focuses on learning-based approaches for the quantification of blood flow in 4D digital subtraction angiography and is carried out in […]
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11DEC 2020
Deep Learning WS 20/21
Watch now: Deep Learning: Architectures – Part 5 (WS 20/21)
This video discusses learning to learn options for architecture search and first results. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science
Read more about Watch now: Deep Learning: Architectures – Part 5 (WS 20/21)
10DEC 2020
Pattern Recognition WS 20/21
Watch now: Pattern Recognition: Episode 24 – Duality in Optimization (WS 20/21)
In this video, we look into the concept of duality in optimization and strong duality in convex problems. Watch on:FAU TVYouTube
Read more about Watch now: Pattern Recognition: Episode 24 – Duality in Optimization (WS 20/21)
10DEC 2020
Deep Learning WS 20/21
Watch now: Deep Learning: Architectures – Part 4 (WS 20/21)
This video demonstrates the many uses of residual connections in deep networks from Inception-ResNet to DenseNet. Watch on:FAU TVFAU TV (no memes)YouTube Read the Transcript (Summer 2020) at:LMETowards Data Science
Read more about Watch now: Deep Learning: Architectures – Part 4 (WS 20/21)