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Found 5 papers, 1 papers with code
FBA-Net: Foreground and Background Aware Contrastive Learning for Semi-Supervised Atrium Segmentation
Specifically, we leverage the contrastive loss to learn representations of both the foreground and background regions in the images.
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
Mixture Modeling of Global Shape Priors and Autoencoding Local Intensity Priors for Left Atrium Segmentation
Here, we propose a maximum-a-posteriori formulation that relies on a generative image model by incorporating both local intensity and global shape priors.
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Statistical shape modeling (SSM) has proven useful in many areas of biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
Deep Learning for End-to-End Atrial Fibrillation Recurrence Estimation
In this paper, we propose a machine learning approach that uses deep networks to estimate AF recurrence by predicting shape descriptors directly from MRI images, with NO image pre-processing involved.
