TeachingSs2014MLMIPraktikum

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

Practical Course:
Machine Learning in Medical Imaging

Nassir Navab, Tingying Peng, Lichao Wang, Loic Peter, Sebastian Poelsterl, Benjamin Gutierrez Becker

Type: Master Practical Course Module IN2106
SWS: 6
ECTS: 10 Credits
Location: MI 03.13.010
Time: Tuesday 14:00-15:30
Course Language: English

Announcements

  • July, 11th: Final presentation are scheduled on 15th of July, 14:00-15:30pm in MI 03.13.010
  • May, 28th: Mid-term presentation are rescheduled on 17th of June
  • May, 1st: Projects have been assigned. Please contact your supervisor as soon as possible to setup an initial meeting.

About the Course

In this course students will learn through practice the application of different machine learning methods to problems involving medical images. The Master-Praktikum will consist in:

  • (1) a few introductory lectures on machine learning and its application in different problems involving medical imaging,
  • (2) a number of exercises to apply different learning approaches to toy example data,
  • (3) a machine learning project with a real medical application to be solved in groups (pairs).

Regular meetings will be held individually with each group to follow up the progress of the project along the semester

Evaluation

  • Homework attestations (20%)
  • Practical implementation of the project (50%)
  • Midterm (5%) and final (15%) Presentations:Use the CAMP templates for PowerPoint CAMPmaster.pot, or Latex: CAMP-latex-template.zip.
  • Report (10%): Maximum 8 pages. Please download and use the llncs2e.zip Latex template.

Registration

  • Interested students should attend the introductory meeting to enlist in the course.
  • Students can only register through TUMOnline themselves if the maximum number of participants hasn't been reached by end of February.
  • Maximum number of participants: 20.

Requirements

  • Basic knowledge in MATLAB and C/C++ programming is an advantage.

Preliminary Schedule

Date Topic By Slides Homework
28.01.2014 Introduction Meeting all Info Meeting Slides
Introduction to Probability
 
08.04.2014 Image Representation and Feature Extraction Loic Peter Slides Dataset
Exercise sheet
15.04.2014 Linear Classifiers and Support Vector Machines (SVMs) Sebastian Pölsterl Slides
Projects
Exercise sheet
Data
IRLS
29.04.2014 Evaluation Measures and Bootstrap Sebastian Pölsterl Slides Exercise sheet
06.05.2014 Boosting Tingying Peng Slides Exercise sheet
MATLAB Code
13.05.2014 Random Forests Loic Peter Slides Exercise sheet
Code
27.05.2014 Principal Component Analysis (PCA) and Statistical Shape Models Ahmad Ahmadi Slides Exercise sheet
Matlab Code
03.06.2014 Clustering Lichao Wang Slides Exercise sheet
Data
17.06.2014 Midterm Presentations All tutors    
15.07.2014 Final Presentations All tutors    

Projects

Group Project Supervision
Imran Badshah Mashood, Xiao Huang Deep Learning Sebastian
Hasan Sarhan, Ahme El-Fiky Multi-modal registration for correlative microscopy using image analogies Benjamin
Yeshaswini Nagaraj Deepa, Darshini Gunashekar Sparse Multi-Modal Hashing Benjamin
Ana Artemisa López, Pedro Agustín Gómez Damián Multiple Sclerosis Lesion Segmentation Loic
Anca Stefanoiu, Viswanath Pamulakanty Sudarshan Modality Propagation using Random Forests Loic
Muhamed Bilal Javed, Toluwalope Gbakinro Multi-Sequence MRI Segmentation using Boosting Tingying
Catalina Raymond Guzman, Sindhushree Jayashankar Multi-Sequence MRI Segmentation using Dictionary Learning Tingying
Sing Chun Lee, Sasa Cheng Multi-Task Learning Sebastian
Patmaa Sridharan, Phalgun Chowdhary Chintala Context-Specific Classification for Prostate Segmentation Lichao
Fatemeh Nejatbakhshesfahani, Alexandra Lungu, Zhengyu Shan Context Learning for Label Prediction in Cardiac MRI Lichao

TeachingForm
Title: Machine Learning in Medical Imaging
Professor: Prof. Dr. Nassir Navab
Tutors: Tingying Peng, Lichao Wang, Loic Peter, Sebastian Poelsterl, Benjamin Gutierrez Becker
Type: Praktikum
Information: 10 ECTS credits
Term: 2014SoSe
Abstract:  


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