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Technical University of Munich
Technical University of Munich

Machine Learning in Medical Imaging

Overview

  • The aim of the course is to provide the students with notions about various machine learning techniques. The course is mainly defined by a project.
    • 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 preliminary meeting is scheduled for July 5th, from 14:00 to 14:30, with the following Zoom link:

https://tum-conf.zoom.us/j/63320903512?pwd=WEljYUtYUERHSFhzV1d1N2Iwd2JOZz09

Registration

Master Practical Course - Machine Learning in Medical Imaging (IN2106, IN4142)

Lecturer (assistant)
Number0000001192
TypePractical course
Duration6 SWS
TermWintersemester 2023/24
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline

Dates

Admission information

See TUMonline
Note: For registration, you have to be identified in TUMonline as a student. Interested students should attend the preliminary meeting. This semester we will have a joint presentation of the MLMI and DLMA courses offered, on Wednesday, July 5th, 2023, from 14:00 to 15:00 hrs with the following agenda: Machine Learning in Medical Imaging (MLMI): 14:00 hrs. - 14:30 hrs. Deep Learning for Medical Applications (DLMA): 14:30 hrs. - 15:00 hrs. Feel free to attend all the talks of your interest. The sessions will be conducted in Zoom: https://tum-conf.zoom.us/j/63320903512?pwd=WEljYUtYUERHSFhzV1d1N2Iwd2JOZz09 Please find the latest information on the course Wiki: https://wiki.tum.de/display/mlmi/MLMI+Winter+2023-2024

Description

This Master-Praktikum will consist in: (1) a few introductory lectures on machine learning and its application in different medical imaging applications, (2) a few exercises to apply different learning approaches in toy examples and (3), a machine learning project with a real medical application.

Prerequisites

* Strong knowledge of Python * Knowledge of main concepts of object oriented programming, basic software engineering. * Knowledge of deep learning (should have taken an introductory course level)

Examination

Exercises + Final Project

Links

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