TeachingWs18MLMI

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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    
03.12.2018 Graph Convolutional Networks (GCN) Anees Slides Hands-on
10.12.2018 Recurrent Neural Networks (RNN/LSTM) Anjany    
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




TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Prof. Nassir Navab
Tutors: Dr. Shadi Albarqouni, Christoph Baur, Magda Paschali, Huseyin Coskun, Abhijit Guha-Roy, Anees Kazi, Roger Soberanis, Anjany Sekuboyina
Type: Praktikum
Information: 6 SWS, 10 ECTS Credits (Module IN2106)
Term: 2018WiSe
Abstract:  


Edit | Attach | Refresh | Diffs | More | Revision r1.26 - 14 Dec 2018 - 10:40 - ShadiAlbarqouni

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