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Found 6 papers, 3 papers with code
EchoNarrator: Generating natural text explanations for ejection fraction predictions
It then feeds the attributes into a large language model to generate text that helps to explain the network's outcome in a human-like manner.
Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approach
Although deep learning techniques have been successful in achieving this, they still struggle with fully verifying the suitability of an image for specific measurements due to factors like the correct location, pose, and potential occlusions of cardiac structures.
Towards Robust Cardiac Segmentation using Graph Convolutional Networks
We propose a graph architecture that uses two convolutional rings based on cardiac anatomy and show that this eliminates anatomical incorrect multi-structure segmentations on the publicly available CAMUS dataset.
Shape-based pose estimation for automatic standard views of the knee
The characteristics of the standard views of the knee suggests that the shape information of individual bones could guide an automatic positioning procedure, reducing time and the amount of unnecessary radiation during C-arm positioning.
Light-weight spatio-temporal graphs for segmentation and ejection fraction prediction in cardiac ultrasound
In particular, segmentations of the left ventricle can be used to derive ventricular volume, ejection fraction (EF) and other relevant measurements.
Ranked #3 on
LV Segmentation
on Echonet-Dynamic
Automatic Plane Adjustment of Orthopedic Intra-operative Flat Panel Detector CT-Volumes
The most important benefit of the MTL approach is that it is a single network for standard plane regression for all body regions with a reduced number of stored parameters.
