Artificial Intelligence in Cardiovascular Medicine (AICM)
Repos of the AICM-Group, Heidelberg University Hospital
Germany
Pinned Repositories
adaptor_docker_example
A docker example repository to support participants in creating submissions for the AdaptOR2022 MICCAI Challenge https://www.synapse.org/AdaptOR_Challenge_2022_MICCAI
CADP
cmr-domain-adaptation
Unsupervised Domain Adaptation (AX2SAX) for CMR images, Koehler et al. 2021, IEEE TMI
cmr-landmark-detection
Semantic Segmentation of CMR with a U-Net based architecture. Implemented in TF2.X. Trainings, prediction and evaluation scripts/notebooks for heatmap based right ventricle insertion point detection on cine CMR images. Koehler et al. 2022, BVM
endovis-ml
Visualization of dataset splits for surgical phase and instrument recognition
fed-foundation-model-cardiac-ct
FUNAvg
Tölle et al. (2024). FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels. MICCAI.
mfvi-dip-mia
Posterior temperature optimized Bayesian models for inverse problems in medical imaging, Laves et al. (2022), Medical Image Analysis
mvhota
A multi-view higher order tracking accuracy metric to measure temporal and spatial associations in multi-point tracking
suture-detection-pytorch
Point detection through multi-instance deep heatmap regression for sutures in endoscopy, Sharan et al. 2021, IJCARS
Artificial Intelligence in Cardiovascular Medicine (AICM)'s Repositories
Cardio-AI/endovis-ml
Visualization of dataset splits for surgical phase and instrument recognition
Cardio-AI/mfvi-dip-mia
Posterior temperature optimized Bayesian models for inverse problems in medical imaging, Laves et al. (2022), Medical Image Analysis
Cardio-AI/cmr-domain-adaptation
Unsupervised Domain Adaptation (AX2SAX) for CMR images, Koehler et al. 2021, IEEE TMI
Cardio-AI/cmr-landmark-detection
Semantic Segmentation of CMR with a U-Net based architecture. Implemented in TF2.X. Trainings, prediction and evaluation scripts/notebooks for heatmap based right ventricle insertion point detection on cine CMR images. Koehler et al. 2022, BVM
Cardio-AI/suture-detection-pytorch
Point detection through multi-instance deep heatmap regression for sutures in endoscopy, Sharan et al. 2021, IJCARS
Cardio-AI/fed-foundation-model-cardiac-ct
Cardio-AI/adaptor_docker_example
A docker example repository to support participants in creating submissions for the AdaptOR2022 MICCAI Challenge https://www.synapse.org/AdaptOR_Challenge_2022_MICCAI
Cardio-AI/cmr-phase-detection
Self-supervised method for cardiac phase detection in 4D CMR. Model consists of a deformable registration part, the derivation of the per-voxel deformation angle and a rule-set based on the physiological properties of a contracting ventricle. Implemented in TF2.X. Koehler et al. 2022, STACOM workshop @ MICCAI
Cardio-AI/FUNAvg
Tölle et al. (2024). FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels. MICCAI.
Cardio-AI/detcyclegan_pytorch
Mutually improved endoscopic image synthesis and landmark detection in unpaired image to image translation Sharan et al. 2021, IEEE JBHI
Cardio-AI/mvhota
A multi-view higher order tracking accuracy metric to measure temporal and spatial associations in multi-point tracking
Cardio-AI/CADP
Cardio-AI/depth-sensor-evaluation
Cardio-AI/fl-multi-modal-dataset-creation
Cardio-AI/foundation-models-lecture
Cardio-AI/kaapana
Kaapana (from the hawaiian word kaʻāpana, meaning “distributor” or “part”) is an open source toolkit for state of the art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging.
Cardio-AI/memorization-ldm
Cardio-AI/vae-tutorial