MaLGM

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

Deep Generative Model for Longitudinal Analysis

Supervision: Prof. Dr. Nassir Navab, Dr. Seong Tae Kim

Abstract

Longitudinal analysis of a disease is an important issue to understand its progression as well as to design prognosis and early diagnostic tools. From the longitudinal sample series where data is collected from multiple time points, both the spatial structural abnormalities and the longitudinal variations are captured. Therefore, the temporal dynamics of a disease are more informative than static observations of the symptoms, in particular for neuro-degenerative diseases whose progression span over years with early subtle changes. In this project, we will develop a deep generative method to model the lesion progression over time.

Requirements:

  • Good understanding of statistics and machine learning methods.
  • Very good programming skills in Python & TensorFlow? / PyTorch?

Location:

  • Garching

ProjectForm
Title: Deep Generative Model for Longitudinal Analysis
Abstract: Longitudinal analysis of a disease is an important issue to understand its progression as well as to design prognosis and early diagnostic tools. From the longitudinal sample series where data is collected from multiple time points, both the spatial structural abnormalities and the longitudinal variations are captured. Therefore, the temporal dynamics of a disease are more informative than static observations of the symptoms, in particular for neuro-degenerative diseases whose progression span over years with early subtle changes. In this project, we will develop a deep generative method to model the lesion progression over time.
Student: Umut Küçükaslan
Director: Prof. Dr. Nassir Navab
Supervisor: Dr. Seong Tae Kim
Type: Master Thesis
Area: Machine Learning, Medical Imaging
Status: running
Start:  
Finish:  
Thesis (optional):  
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Edit | Attach | Refresh | Diffs | More | Revision r1.3 - 23 Sep 2020 - 10:02 - SeongTaeKim