Practical Course:
Machine Learning in Medical Imaging

Prof. Nassir Navab, Dr. Shadi Albarqouni, Christoph Baur, Magda Paschali, Huseyin Coskun, Abhijit Guha-Roy, Anees Kazi, Roger Soberanis, Anjany Sekuboyina

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: CAMP Seminar Room, 03.13.010
Time: Mondays, 12-14
Course Language: English

Announcements:

• 20-10-2018: Projects are assigned!
• 28-09-2018: Lectures starts at 12 o'clock!
• 28-09-2018: Grading is updated! 60% Project Progress (Done by your tutor -- mainly on your weakly progress on lrz git repository), and 40% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentations skill, progress so far comapred to other groups ...etc.)
• 28-09-2018: Projects will be announced soon (second week of October)
• 29-06-2018: Registration, through TUM Matching Platform, should be done between 29.06. to 04.07.18
• 19-06-2018: A tentative schedule is up!
• 16-06-2018: An introductory meeting: Wednesday, 27.06.2018 (12:00-12:30) in CAMP Seminar Room, 03.13.010.
• 16-06-2018: Website is up!

About the Course

The aim of the course is to provide the students with notions about various machine learning techniques. The course is subdivided into a lecture/excercises block and a project.

• The lectures will include topics in image processing and analysis, unsupervised methods, and supervised methods. Each lecture will be followed by a practical hands-on exercise (e.g. implementation in Python).
• The topics of the projects will be distributed at the beginning of the semester. Each topic will be supervised by a different person. The projects are to be realized by couples. The programming language for the projects is to be decided in agreement with your supervisor.

Registration

• Interested students should attend the introductory meeting to enlist in the course.
• Students can only register through TUM Matching Platform themselves if the maximum number of participants hasn't been reached (please pay attention to the Deadlines).
• A maximum number of participants: 20.

Requirements

• Good knowledge of machine learning methods and terminology: knowledge of the meaning of the words classification, regression, data-set, cross-validation ...
• Strong knowledge of Python programming language
• Background in image processing
• Basic knowledge of C++ is advantageous (in case you decide to use c++ for your final project): knowledge of main concepts of object-oriented programming, basic software engineering, image processing libraries and linear algebra libraries.

Schedule

Date	Topic	By	Slides	Homework
27.06.2018	Preliminary Meeting	Shadi	Slides
15.10.2018	Machine Learning I --> Update: There is No Class on the first day!	Shadi
22.10.2018	Intro. to Deep Learning	Shadi	Slides	Read Chapter 5 in Deep Learning (Goodfellow et al.)
29.10.2018	Neural Networks (NN)	Roger	Slides	Pre-lab: check your email for installing dependencies Hands-on
05.11.2018	Convolutional Neural Networks (CNN)	Shadi	Slides	Hands-on
12.11.2018	Convolutional Auto-Encoders (CAE)	Abhijit	Slides	Hands-on
19.11.2018	Generative Adversarial Networks (GAN)	Christoph	Slides	Hands-on Hands-on Solution
26.11.2018	Security of Deep Learning under Adversarial Settings	Magda	Slides	Hands-on
03.12.2018	Graph Convolutional Networks (GCN)	Anees	Slides	Hands-on
10.12.2018	Recurrent Neural Networks (RNN/LSTM)	Anjany	Slides	Hands-on
17.12.2018	Intermediate Presentation	all tutors
07.01.2019	Tentative
14.01.2019	Tentative
21.01.2019	Tentative
28.01.2019	Final Presentation	all tutors

Evaluation

• 60% Project Progress (Done by your tutor -- mainly on your weekly progress on lrz git repository)
• 40% Intermediate and Final Presentation (Done by all tutors -- mainly on your presentation skill, progress so far compared to other groups ...etc.)
• Presentation: Use the CAMP templates for PowerPoint camp-tum-jhu-slides.zip, or Latex: CAMP-latex-template.

Projects

ID	Project	Tutor	Group I	Group II	Material
1	ICLR 2019 Reproducibility Challenge Applied to Skin Lesion Classification	Magda	Ansari, Aydieva	Mazumder, Mittal	Project Description
2	Fundus Image Analysis and Grading for Diabetic Retinopathy	Hasan	Kotova, Krishnan	Raja, Beedu	Project Description
3	Inception Graph Convolutional Network for Parkinson’s Disease Prediction.	Anees	Rahman, Kubilay	Puneet, Chabert	Project Description
4	Polyp Characterization in Colonoscopy Videos	Roger \ Fernando	Algunineedy, Aneja	Lem Carrillo, Prabha	Project Description
5	Generative modelling and feature interpretation using VAEs and GANs	Anjany	Rumney, Mohanty	Elskhawy, Colelli	Project Description