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Found 8 papers, 2 papers with code
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning
By analyzing thousands of subsets from seven medical datasets representing ultrasound, X-ray, CT, and pathology images, we found that the best correlates of performance were not size or class balance but $A$ -- ``big alpha'' -- a set of generalized entropy measures interpreted as the effective number of image-class pairs in the dataset, after accounting for similarities among images.
The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)
Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed.
$\textit{greylock}$: 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.
