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 18, 2018, 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: Thursday, June 21, 2018, 16:00-18:00
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 you could not register still come to our introductory meeting to see if there are papers free. 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

Please get in contact with your advisor as soon as possible, and at least 4 weeks before your presentation

Submit your presentation outline to your advisor at least 2 weeks before your presentation and meet with your advisor.

Submit your presentation slides to your advisor at least 1 week before your presentation and meet again.

All participants must read all papers and answer a few questions. The questions will be available here. The answers must be sent to the corresponding advisor until 20.07.2018, 18:00. We highly recommend to read and understand all papers first, before you start to prepare your presentation.

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

Time Paper Presenting student   Advisor Slides
Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images
- nice paper: achieves better performance on Gland segmentation challenge with semi-supervised learning - adversarial only sees predicted segmentations, but has to decide whether they come from the labelled or unlabelled training set.
Diane Wagner Sudhanshu Mittal
Detect to Track and Track to Detect
Conceptional simple but powerful tracking architecture. Tracking is in important task in many biomedical applications.
Ahmad Bashiti Özgün Çiçek
Neural Discrete Representation Learning
Combines a variational auto encoder with vector quantization. Nice approach to learn powerful representations from unlabelled data.
Juan Sebastian Diaz Osorio Mohammadreza Zolfaghari
Population Based Training of Neural Networks
Fast and powerful approach to adapt hyper-parameters during training and to optimize networks using non-differentiable loss functions.
Lior Fuks Artemij Amiranashvili
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
Currently top performing method on the Cityscapes semantic segmentation benchmark.
Julia Abels Gabriel Leivas Oliveira