EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms

1 code implementation • 23 Jul 2023 • Masoud Mokhtari, Mobina Mahdavi, Hooman Vaseli, Christina Luong, Purang Abolmaesumi, Teresa S. M. Tsang, Renjie Liao

To address this challenge, we introduce an echocardiogram-based, hierarchical graph neural network (GNN) for left ventricle landmark detection (EchoGLAD).

Graph Representation Learning

Echo-SyncNet: Self-supervised Cardiac View Synchronization in Echocardiography

1 code implementation • 3 Feb 2021 • Fatemeh Taheri Dezaki, Christina Luong, Tom Ginsberg, Robert Rohling, Ken Gin, Purang Abolmaesumi, Teresa Tsang

In echocardiography (echo), an electrocardiogram (ECG) is conventionally used to temporally align different cardiac views for assessing critical measurements.

One-Shot Learning Self-Supervised Learning

U-LanD: Uncertainty-Driven Video Landmark Detection

no code implementations • 2 Feb 2021 • Mohammad H. Jafari, Christina Luong, Michael Tsang, Ang Nan Gu, Nathan Van Woudenberg, Robert Rohling, Teresa Tsang, Purang Abolmaesumi

We tackle a specifically challenging problem, where training labels are noisy and highly sparse.

Reciprocal Landmark Detection and Tracking with Extremely Few Annotations

no code implementations • CVPR 2021 • Jianzhe Lin, Ghazal Sahebzamani, Christina Luong, Fatemeh Taheri Dezaki, Mohammad Jafari, Purang Abolmaesumi, Teresa Tsang

The model is trained using few annotated frames across the entire cardiac cine sequence to generate consistent detection and tracking of landmarks, and an adversarial training for the model is proposed to take advantage of these annotated frames.