cardio-ai Goto Github PK

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

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

depth-sensor-evaluation

detcyclegan_pytorch

Mutually improved endoscopic image synthesis and landmark detection in unpaired image to image translation Sharan et al. 2021, IEEE JBHI

endovis-ml

Visualization of dataset splits for surgical phase and instrument recognition

fed-foundation-model-cardiac-ct

fl-multi-modal-dataset-creation

foundation-models-lecture

funavg

Tölle et al. (2024). FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels. MICCAI.

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.

memorization-ldm

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

vae-tutorial