Left Ventricle Segmentation
4 papers with code • 2 benchmarks • 1 datasets
Most implemented papers
End-to-end Learning of Convolutional Neural Net and Dynamic Programming for Left Ventricle Segmentation
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.
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.
Contrastive Pretraining for Echocardiography Segmentation with Limited Data
Our results show that contrastive pretraining helps improve the performance on left ventricle segmentation, particularly when annotated data is scarce.
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis
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.