Popular GitHub Repositories for Cardiac

MICCAI 2023 code for the paper: Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis. EchoDiffusion is a collection of video diffusion models trained from scratch on the EchoNet-Dynamic dataset with the imagen-pytorch repo.

[MICCAI'18] Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences

A self-supervised foundation ECG model for broad and scalable cardiac applications

cardiAc ultrasound Segmentation & Color-dopplEr dealiasiNg Toolbox (ASCENT)

[IEEE-TMI 2021] This is our PyTorch implementation for Adapt Everywhere paper on unsupervised domain adaptation using entropy and point-cloud paper.

EP-PINNs implementation for 1D and 2D forward and inverse solvers for the Aliev-Panfilov cardiac electrophysiology model. Also includes Matlab finite-differences solver for data generation.

Code for the analysis of cardiac motion and cardiac pathology classification

? Code for "Neural network-based integration of polygenic and clinical information: Development and validation of a prediction model for 10 year risk of major adverse cardiac events in the UK Biobank cohort" ?

4th place solution of Multi-Centre, Multi-Vendor, Multi-Disease Cardiac Image Segmentation Challenge

Deep learning based cardiac segmentation

Automatically segment the left Atrium in cardiac images

Predict a bounding box around the heart in X-ray images.

[MICCAI 2022] Official Implementation for "Meta-hallucinator: Towards few-shot cross-modality cardiac image segmentation"

[ICML 2022] Active learning of AI models on ECG data with SoQal

A dynamic attentive graph model for cardiac MRI image reconstrunction of CMRxRecon dataset with PromptUnet for sensitivity map estimation.

Novel Deep Neural Network for Sophisticated Cardiac Segmentation - SOTA. Published in MAPR 2024 and IEEE

Explainable Automated Machine Learning Framework for Predicting the Risk of Major Adverse Cardiac Event (MACE)

The Heart MRI Image-Left Atrial Segmentation dataset is a comprehensive collection of cardiac magnetic resonance imaging (MRI) data specifically focused on the segmentation of the left atrium. The dataset aims to facilitate research and development in the field of cardiac image analysis, particularly for automated segmentation.

A deep learning (DL) pipeline to simultaneously predict multi-chamber segmentation and all cardiac imaging planes. Author: Zhennong Chen, PhD

Deep Learning Scientific Research - Detecting Cardiac pathologies on ECG (electrocardiogram) using neural networks