Medical Deep Learning Lab: IMI

IMI | Institut für Medizinische Informatik

Medical Deep Learning

Ongoing Projects

JOINT: Joint Disorders in Paediatric Orthopaedics with Interactive 3D Ultrasound Navigation – Human + Technology (BMFTR)

LABEL: Automatic labelling of anatomies in large-scale medical image datasets using self-supervised multimodal learning
(DFG)

Completed Projects

Kids Bone Checker: Deep Learning for Precise Pediatric Fracture Diagnosis and Therapy Recommendation (Land SH)

MEDICARE: Motion intEgrateD aI-based CardiAc mRi rEconstruction (BMBF, MESRI)

AutoSAFE: AI-guided assistance system for the diagnosis of infantile forearm fractures
(BMBF)

MEIDIC-VTACH Collaborative Project: Mobile Electrocardiographic Imaging for the Prediction of Ventricular Tachycardia (BMBF, ERA-CVD)
(BMBF, ERA-CVD)

ARTEMIS: Artificial intelligence musculoskeletal disorders study
(BMBF-Project)

OncoReg - Challenge evaluation for the integration of expert anatomical knowledge from retrospective data in oncological computed tomographic image registration with artificial intelligence (BMBF)

MDMLA: Multi-task Deep Learning for Large-scale Multimodal Biomedical Image Analysis
(funded by BMBF)

CoCoAI: Cooperative and Communicating AI methods for medical image-guided diagnostics
(funded by BMBF)

VIKOOB: Visual context information for optimising ventilation therapy
(funded by Federal Ministry of Economics and Technology)

KI-RAD: Artificial intelligence for radiological imaging in emergency Artificial intelligence for radiological imaging in emergency and intensive care (BMWi-Project)

Learning Contrast-invariant Contextual Local Descriptors and Similarity Metrics for Multi-modal Image Registration
(funded by German Research Foundation)

Geometric deep learning and graphical models for accurate and traceable medical image analysis

Towards Realtime MR-guided Motion Compensation using Model-based Registration without Fiducial Markers
(funded by German Research Foundation)

MRI-based pseudo-CT synthesis for attenuation correction in PET-MRI and Linac-MRI

Learning to Predict Stroke Outcome based on Multivariate CT Images