Block-Seminar on Deep Learning for Bio-Medical Image Analysis

apl. 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.

(2 SWS)
Room: t.b.a.
Places: max. 10 students
Contact person: Özgün Çiçek

Introduction: Wednesday, April 26, 2017, 10:15
Room: 52-2-17
Introduction and allocation of seminar topics
Will be held jointly with Seminar on Current Works in Computer Vison

Mid-Semester Meeting: t.b.a.
Room: t.b.a.
Introduction to Neural Networks by apl. Prof. Olaf Ronneberger (Google DeepMind)

ECTS Credits: 4

Recommended semester:

6 (Bachelor), any (Master)
Requirements: Background in computer vision

Remarks: This course is offered to both Bachelor and Master students. The language of this course is English. All presentations must be given in English.

Topics will be assigned in the first meeting. Please register for the seminar online before the first meeting. If there are more interested students than places, places will be assigned by a mixture of motivation in the first meeting and priority suggestions of the system. The date of registration is NOT important. In particular, we want to avoid that people grab a topic and then jump off during the semester. Please have a coarse look at all available papers to make an informed decision in the first meeting.

There is a related Seminar on Current Works in Computer Vision offered by Prof. Thomas Brox

All participants must read all papers and answer a few questions. The questions will be available here within a month. The answers must be sent to the corresponding adviser until mid-semester (t.b.a.). We highly recommend to read and understand all papers first, before you start to prepare your presentation.

Slides of first session with instructions for a good presentation
Powerpoint template (optional)


Papers (please note that we only have 10 places):

Time Paper Presenting student   Advisor Slides
Pixelwise Instance Segmentation with a Dynamically Instantiated Network
Another instance segmentation approach. Shows failure cases of FCIS.
Jan Bechtold Anton Böhm
Optimization as a model for few-shot learning
Nice approach for few-shot learning. Limited training data sets is one of the main problems in the for biomedical domain.
Lukas Voegtle Thorsten Falk
Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees
Interpretability of network decisions is a hot topic in the medical field.
Katrin Baumgaertner Ahmed Abdulkadir