Left Ventricle Segmentation
5 papers with code • 2 benchmarks • 1 datasets
SUN09
Latest papers
SimLVSeg: Simplifying Left Ventricular Segmentation in 2D+Time Echocardiograms with Self- and Weakly-Supervised Learning
From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate segmentation of the heart structures allows doctors to assess the heart's condition and devise treatments with greater precision and accuracy.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
ayanglab/hdl
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A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexity of anatomical and biochemical phenomena producing them in reality.
Contrastive Pretraining for Echocardiography Segmentation with Limited Data
biomedia-mbzuai/contrastive-echo
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Our results show that contrastive pretraining helps improve the performance on left ventricle segmentation, particularly when annotated data is scarce.
Curriculum semi-supervised segmentation
This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region.
End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation
minhnhat93/EDPCNN
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MIDL 2019
Differentiable programming is able to combine different functions or programs in a processing pipeline with the goal of applying end-to-end learning or optimization.
