AG Midas
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)
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Otfried-Müller-Str. 3
72016 Tübingen
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Prof. Dr.-Ing. Thomas Kuestner |
07071 29-80507 |
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Sekretariat: |
07071 29-86676 |
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Sekretariat: |
07071 29-85841 |
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People
Leitung MIDAS LAB, University Hospital Tübingen, Department of Radiology
E-Mail-Adresse: thomas.kuestner@med.uni-tuebingen.de
Publikationen: Publikationen
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
Further group members
University Hospital Tübingen, Department of Radiology
E-Mail-Adresse: louisa.fay@med.uni-tuebingen.de
Publikationen: Publikationen
University Hospital Tübingen, Department of Radiology
E-Mail-Adresse: aya.ghoul@med.uni-tuebingen.de
Publikationen: Google Scholar
University Hospital Tübingen, Department of Radiology
E-Mail-Adresse: veronika.ecker@med.uni-tuebingen.de
Publikationen: Publikationen
Patrick Putzky, MSc
Max Planck Institute for Intelligent Systems, Empirical Inference Department
E-Mail-Adresse: patrick.putzky@tuebingen.mpg.de
Publikationen: Publikationen
Personenprofil: Mehr zur Person
Erick Cobos, MSc
Max Planck Institute for Intelligent Systems, Empirical Inference Department
E-Mail-Adresse: erick.cobos@med.uni-tuebingen.de
Publikationen: Publikationen
Personenprofil: Mehr zur Person
University Hospital Tübingen, Department of Radiology
E-Mail-Adresse: siying.xu@med.uni-tuebingen.de
Publikationen: Publikationen
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
Uddeshya Upadhyay, MSc
University Hospital Tuebingen &
University of Tuebingen, AI Cluster, Explainable Machine Learning
E-Mail-Adresse: uddeshya.upa@gmail.com
Personenprofil: Mehr zur Person
Daniel Amsel
University Hospital Tübingen, Department of Radiology & Siemens Healthineers
E-Mail-Adresse: daniel.amsel@uni-tuebingen.de
Majd Helo
University Hospital Tübingen, Department of Radiology & Siemens Healthineers
E-Mail-Adresse: majd.helo@uni-tuebingen.de
Jonas Petersen
University Hospital Tübingen, Department of Radiology & Siemens Healthineers
E-Mail-Adresse: jonas.petersen@uni-tuebingen.de
Alumni
Tobias Hepp
Marc Fischer
Dominik Blum
Karim Armanious
Annika Liebgott
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
Zertifikate und Verbände
Focus: Top Nationales Krankenhaus 2023
Kooperation für Transparenz und Qualität im Gesundheitswesen
Qualitätspartnerschaft mit der PKV
Erfolgsfaktor Familie
Die Altersvorsorge für den Öffentlichen Dienst