Block-Seminar on Deep Learning for Bio-Medical Image Analysisapl. Prof. Olaf Ronneberger (Google DeepMind)
In this seminar you will learn about relevant bio-medical research fields and the most recent methods (mainly based on deep learning) that have been already applied to bio-medical data, or that have a large potential in this field. For each paper there will be one person, who performs a detailed investigation of a research paper and its background and will give a presentation. The presentation is followed by a discussion with all participants about the merits and limitations of the respective paper. You will learn to read and understand contemporary research papers, to give a good oral presentation, to ask questions, and to openly discuss a research problem.
Slides of first session with instructions for a good presentation
Powerpoint template (optional)
Papers (please note that we only have 10 places):
|Time (Duration)||Paper||Presenting student||Advisor||Slides|
|10:00 (40 min)||Wide Residual Networks
Making networks wider instead of deeper works very well.
|David-Elias Künstle||Anton Böhm||Slides|
|10:40 (40 min)||Progressive Neural Networks
Transfer learning without forgetting. Nice alternative over a simple fine-tuning which tends to forget the previous task quickly.
|Osama Makansi||Christian Zimmermann|
|11:20 (40 min)||Discussion|
|12:00 (60 min)||Lunch Break|
|13:00 (40 min)||Adversarial Feature Learning
Interesting approach to learn a reasonable feature representation with adversarial networks.
|Ludwig Striet||Robert Bensch|
|13:40 (40 min)||Unsupervised Learning of 3D Structure from Images
Nice overview and application of recent conditional generative models.
|Andre Biedenkapp||Özgün Cicek|
|14:20 (40 min)||Early Visual Concept Learning with Unsupervised Deep Learning
Learning disentangled representations is a key concept for unsupervised learning.
|Aditya Bhatt||Mohammadreza Zolfaghari|
|15:00 (40 min)||Discussion|