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Found 6 papers, 1 papers with code
$\textit{greylock}$: A Python Package for Measuring The Composition of Complex Datasets
However, there exists a richer and potentially more useful set of measures, termed diversity measures, that incorporate elements' frequencies and between-element similarities.
Are foundation models efficient for medical image segmentation?
Foundation models are experiencing a surge in popularity.
Label-free segmentation from cardiac ultrasound using self-supervised learning
We also tested against external images from an additional 10, 030 patients with available manual tracings of the left ventricle.
Domain-guided data augmentation for deep learning on medical imaging
While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date.
Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions
Using 685 retrospectively collected echocardiograms from fetuses 18-24 weeks of gestational age from 2000-2018, we trained convolutional and fully-convolutional deep learning models in a supervised manner to (i) identify the five canonical screening views of the fetal heart and (ii) segment cardiac structures to calculate fetal cardiac biometrics.
Fast and accurate classification of echocardiograms using deep learning
The essential first step toward comprehensive computer assisted echocardiographic interpretation is determining whether computers can learn to recognize standard views.
