Tutorials

Folgt demnächst.

Für Sonntag Nachmittag, 07.03.2021, 14:00-17:00 Uhr sind vier Tutorials geplant:

Grundlegende Vorkenntnisse über Neuronale Netze und Deep Learning sinnvoll:

Jens Petersen, Gregor Köhler, Michael Baumgartner, Maximilian Zenk, Shuhan Xiao, Carsten Lüth
Deutsches Krebforschungszentrum (DKFZ) Heidelberg
Medical Image Computing


The remarkable rise of deep learning has led to an overwhelming amount of new papers coming up by the week. This tutorial intends to filter out the research most relevant for the medical image computing (MIC) community and present it in a structured and understandable form. It will cover recent developments related to common tasks in the community (e.g. segmentation, detection), but will also discuss methods that are currently gaining traction and that are likely to become even more relevant in the future, such as self-supervised learning and transformers. Basic knowledge of neural networks and deep learning is recommended.

Keine oder kaum Vorkenntnisse:

Andreas Maier
Friedrich-Alexander Universität Erlangen-Nürnberg
Lehrstuhl für Mustererkennung


In this tutorial, we try to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

Grundlegende Vorkenntnisse über Neuronale Netze und Deep Learning sinnvoll:
Mit der Bildregistrierung wird ein spezielles Anwendungsgebiet beleuchtet. Theorie + Hands-On mit der Möglichkeit, selbst kleine Experimente am Rechner durchzuführen.

Mattias Heinrich et al.
Universität zu Lübeck
Institut für Medizinische Informatik


Medical image registration has been a cornerstone in the research fields of medical image computing and computer assisted intervention, responsible for many clinical applications. Whilst machine learning methods have long been important in developing pairwise algorithms, recently proposed deep-learning-based frameworks directly infer displacement fields without iterative optimisation for unseen image pairs, using neural networks trained from large population data. These novel approaches promise to tackle several most challenging aspects previously faced by classical pairwise methods, such as high computational cost, robustness for generalisation and lack of inter-modality similarity measures.

Output from several international research groups working in this area include award-winning conference presentations, high-impact journal publications, well-received open-source implementations and industrial-partnered translational projects, generating significant interests to all levels of world-wide researchers. Accessing to the experience and expertise in this inherently multidisciplinary topic can be beneficial to many in our community, especially for the next generation of young scientists, engineers and clinicians who often have only been exposed to a subset of these methodologies and applications.

We propose to organise a tutorial including both theoretical and practical sessions, inviting expert lectures and tutoring coding for real-world examples. Six lectures cover topics from basic methodologies to advanced research directions, with two hands-on sessions guiding participants to understand and implement published algorithms using clinical imaging data. We aim to provide an opportunity for the participants to bridge the gap between expertises in medical image registration and deep learning, as well as to start a forum to discuss know-hows, challenges and future opportunities in this area.

Das Tutorial 4 - Of Bones and Muscles – musculoskeletal human body modelling muss leider entfallen.
Bitte beachten Sie die inhaltliche Änderung von Tutorial T02.

Anmeldung über die Registrierungsseite.
Eine Anmeldung zu den Tutorials ist auch ohne Anmeldung zur Tagung möglich.

Die Anmeldelinks zu den Tutorials wurden am 05.03.2021 an die Angemeldeten geschickt. Am 07.03.2021 gegen 12:30h erfolgt eine erneute Zusendung der Anmeldelinks.

Jens Petersen, Gregor Köhler, Michael Baumgartner, Maximilian Zenk, Shuhan Xiao, Carsten Lüth
Deutsches Krebforschungszentrum (DKFZ) Heidelberg
Medical Image Computing


The remarkable rise of deep learning has led to an overwhelming amount of new papers coming up by the week. This tutorial intends to filter out the research most relevant for the medical image computing (MIC) community and present it in a structured and understandable form. It will cover recent developments related to common tasks in the community (e.g. segmentation, detection), but will also discuss methods that are currently gaining traction and that are likely to become even more relevant in the future, such as self-supervised learning and transformers. Basic knowledge of neural networks and deep learning is recommended.

Andreas Maier
Friedrich-Alexander Universität Erlangen-Nürnberg
Lehrstuhl für Mustererkennung


In this tutorial, we try to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

Mattias Heinrich et al.
Universität zu Lübeck
Institut für Medizinische Informatik


Medical image registration has been a cornerstone in the research fields of medical image computing and computer assisted intervention, responsible for many clinical applications. Whilst machine learning methods have long been important in developing pairwise algorithms, recently proposed deep-learning-based frameworks directly infer displacement fields without iterative optimisation for unseen image pairs, using neural networks trained from large population data. These novel approaches promise to tackle several most challenging aspects previously faced by classical pairwise methods, such as high computational cost, robustness for generalisation and lack of inter-modality similarity measures.

Output from several international research groups working in this area include award-winning conference presentations, high-impact journal publications, well-received open-source implementations and industrial-partnered translational projects, generating significant interests to all levels of world-wide researchers. Accessing to the experience and expertise in this inherently multidisciplinary topic can be beneficial to many in our community, especially for the next generation of young scientists, engineers and clinicians who often have only been exposed to a subset of these methodologies and applications.

We propose to organise a tutorial including both theoretical and practical sessions, inviting expert lectures and tutoring coding for real-world examples. Six lectures cover topics from basic methodologies to advanced research directions, with two hands-on sessions guiding participants to understand and implement published algorithms using clinical imaging data. We aim to provide an opportunity for the participants to bridge the gap between expertises in medical image registration and deep learning, as well as to start a forum to discuss know-hows, challenges and future opportunities in this area.

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