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Medical Informatics laboratory

The Medical Informatics / Signal and Image Processing laboratory focuses on the modern processing of medical image data (e.g. computed tomography, magnetic resonance imaging, ultrasound, ...) using current methods and technologies from the core areas of segmentation, registration, visualisation and artificial intelligence.

The areas of application include

  • Computer-aided diagnosis and therapy
  • Simulation and planning of medical interventions

Other topics that are being worked on in the laboratory include

  • Autonomous driving
  • edge computing

Management

Drechsler, Klaus, Prof. Dr.-Ing.

Teaching Subject: Bildverarbeitung und Medizinische Informatik
Photo Prof. Dr.-Ing. Klaus Drechsler
Professor
Fachbereich 9 - Medizintechnik und Technomathematik
Heinrich-Mußmann-Straße 1
Room 01C02
52428 Jülich

Consultation Hours

N.V.

Employees

Roussel, Johanna M.Sc.

Photo  Johanna Roussel M.Sc.
Wissenschaftliche Angestellte
Fachbereich 9 - Medizintechnik und Technomathematik
Heinrich-Mußmann-Straße 1
Room 01A66
52428 Jülich

Holmes, Tobias M.Sc.

Photo  Tobias Holmes M.Sc.
Wissenschaftlicher Angestellter
Fachbereich 9 - Medizintechnik und Technomathematik
Heinrich-Mußmann-Straße 1
Room 01A75
52428 Jülich

Consultation Hours

nach Absprache

Laboratory members

Elkaam, Hiba Exploring the Role of Q, K, and V Vectors in Vision Transformers: A Practical Study

Hwa, De Shin Exploring the Integration of Key-Value Transformer Models into Vision Transformers for Improved Semantic Segmentation

Amkhaou, Youssef Efficient and Intuitive Background Removal and Chroma Key Spill Reduction in Still Images

Pourgholami Jirandehi, Ali Efficient Image Segmentation through Iterative Axial Projection: A Revision of the IAP U-ViT Model

Bachelor/Master students

Bild zur Person

Marchal, Christoph B.Eng.

Former

Truong, Viet UNet vs. SegNet: A Comparative Analysis using HaNSeg Dataset for Medical Image Segmentation

Lehrke, Jonathan Evaluierung und implementierung von Web GUI Frameworks für die endnutzergerechte Anwendung von Machine Learning in der medizinischen Bildverarbeitung