[JAMA] "Complete AI-Enabled Echocardiography Interpretation with Multitask Deep Learning" by Gregory Holste, Evangelos K. Oikonomou, Márton Tokodi, Attila Kovács, Zhangyang Wang, & Rohan Khera

Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and Quality

Transthoracic echocardiography and mortality in sepsis: analysis of the MIMIC-III database

Myocardial Infarction Detection

[ NeurIPS 2022 ] Official Codebase for "ETAB: A Benchmark Suite for Visual Representation Learning in Echocardiography"

[Communications Medicine] "Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning" by Gregory Holste, Evangelos Oikonomou, Bobak Mortazavi, Zhangyang Wang, and Rohan Khera

[MedIA 2026] MedSapiens: Taking a Pose to Rethink Medical Imaging Landmark Detection

EchoNet-LVH is a deep learning model that quantifies ventricular hypertrophy and predicts etiologies of increased wall thickness and LVH (amyloidosis, HCM, etc).

PyTorch implementation of the two U-Net-based architectures described in "Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography"

Official repository for the paper "ProtoASNet: Dynamic Prototypes for Inherently Interpretable and Uncertainty-Aware Aortic Stenosis Classification in Echocardiography" in MICCAI 2023 Conference

[European Heart Journal] "Severe aortic stenosis detection by deep learning applied to echocardiography" by Gregory Holste et al.

This repository accompanies our paper Unlocking the Heart Using Adaptive Locked Agnostic Networks and enables replication of the key results.

[Pattern Recognition 2026] UltraSeP: Sequence-aware Pre-training for Echocardiography Probe Guidance

Official implementation of Echo-DND: A Dual Noise Diffusion Model for Robust and Precise Left Ventricle Segmentation in Echocardiography

Echo Cardiology Center : "A Comprehensive Tool for Cardiac Echo Reporting" Medical Echo Application “This application is based on the textbook ‘Braunwald’s Heart Disease: A Textbook of Cardiovascular Medicine, 2-Volume Set, 11th Edition.’” This application is developed using Node.js and Bootstrap.

A spatial feedback attention module (FBA) to enhance unsupervised 3D DLIR

The implementation of CLAS-FV described in "Fully automated multi-heartbeat echocardiography video segmentation and motion tracking".

Python package for preprocessing OpenSlide image files and their corresponding annotations for use with Machine Learning segmentation models.

We propose a scalable Temporal Attention Module (TAM) to inject cardiac motion information into segmentation models. TAM captures dynamic changes across temporal frames using multi-headed, cross-temporal attention. It is adaptable across imaging modalities, scalable from 2D to 3D, and adds minimal computational overhead.