AG Midas


midas.lab

Medical Image and Data Analysis

We are an interdisciplinary research group of scientists from the Department of Diagnostic and Interventional Radiology at the University Hospital in Tuebingen, Germany and the Max Planck Institute for Intelligent Systems, Germany.

Our aim is to advance and translate methods and applications for processing and analysis of medical imaging data using artificial intelligence (AI) and machine learning (ML) methods.

We envision imaging applications and computing technology, that provide reliable, explainable and human-interpretable solutions, that enable the integration of AI solutions into clinical practice, and that provide patient-centered workflows for the comprehensive diagnosis and management of neurological, cardiovascular and oncological patients.

Contact

University Hospital of Tuebingen
Diagnostic and Interventional Radiology

Medical Image and Data Analysis (MIDAS.lab)

frontend.sr-only_#{element.contextual_1.children.icon}: Otfried-Müller-Str. 3
72016 Tübingen


Prof. Dr.-Ing. Thomas Kuestner

07071 29-80507

thomas.kuestner@med.uni-tuebingen.de


Sekretariat:
Xenia Helke

07071 29-86676

xenia.helke@med.uni-tuebingen.de


Sekretariat:
Miriam Hohenberger

07071 29-85841

miriam.hohenberger@med.uni-tuebingen.de


frontend.sr-only_#{element.contextual_1.children.icon}: 07071 29-5845


People

Prof. Dr.-Ing. Thomas Küstner

Prof. Dr.-Ing. Thomas Küstner

Leitung MIDAS LAB, University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: thomas.kuestner@med.uni-tuebingen.de

Publikationen: Publikationen

Personenprofil: Mehr zur Person

Portraitfoto

Prof. Dr. med. Dipl.-Math. Sergios Gatidis

Stellvertretende Leitung MIDAS LAB,
Stanford Medicine, Department of Radiology

E-Mail-Adresse: sergios.gatidis@med.uni-tuebingen.de

Publikationen: Publiaktionen

Personenprofil: Mehr zur Person

 Qi Wang

Qi Wang

University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: qi.wang@med.uni-tuebingen.de

Publikationen: Google scholar

Personenprofil: Mehr zur Person

 Louisa Fay

Louisa Fay

University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: louisa.fay@med.uni-tuebingen.de

Publikationen: Publikationen

Personenprofil: Mehr zur Person

 Aya Ghoul

Aya Ghoul

University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: aya.ghoul@med.uni-tuebingen.de

Publikationen: Google Scholar

Personenprofil: Mehr zur Person

 Veronika Ecker

Veronika Ecker

University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: veronika.ecker@med.uni-tuebingen.de

Publikationen: Publikationen

Personenprofil: Mehr zur Person

Frau Siying Xu

Frau Siying Xu

University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: siying.xu@med.uni-tuebingen.de

Publikationen: Publikationen

Personenprofil: Mehr zur Person

Portraitfoto

Carolin Schwitalla, MSc

University Hospital Tuebingen &
University of Tuebingen, Zentrum für Quantitative Biologie (QBiC)

E-Mail-Adresse: carolin.schwitalla@qbic.uni-tuebingen.de

Personenprofil: Mehr zur Person

Portraitfoto

Daniel Amsel

University Hospital Tübingen, Department of Radiology & Siemens Healthineers

E-Mail-Adresse: daniel.amsel@med.uni-tuebingen.de

Portrait

Majd Helo

University Hospital Tübingen, Department of Radiology & Siemens Healthineers

E-Mail-Adresse: majd.helo@med.uni-tuebingen.de

Portrait

Jonas Petersen

University Hospital Tübingen, Department of Radiology & Siemens Healthineers

E-Mail-Adresse: jonas.petersen@med.uni-tuebingen.de

Portrait

Pauline Ornela Megne Choudja

University Hospital Tübingen, Department of Radiology

E-Mail-Adresse: pmegne@aimsammi.org

  • Erick Cobos, MSc 
  • Tobias Hepp
  • Marc Fischer
  • Dominik Blum
  • Karim Armanious
  • Annika Liebgott
  • Marcel Früh
  • Patrick Putzky
  • Uddeshya Upadhyay

Research

Research

Our aim is to advance and translate methods and applications for processing and analysis of medical imaging data using artificial intelligence (AI) and machine learning (ML) methods. We believe in the concept of open and reproducible research. The codes of our projects are collected in our Github repositories. We host the autoPET challenge and provide a large, publicly available training data set.

Specifically, we focus on:

Developments for dynamic and multi-parametric MRI
Development of AI methods for medical imaging
Translation of AI to clinical applications

Learn more about our research Codes and projects

Teaching

weekly

MIDAS.lab seminar

Crona Kliniken, level 3, building 420,
Radiology Department,
Seminar room 410

Current seminar-list

Zertifikate und Verbände

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