31 citations · 93 across the 4 of their papers we have counts for
5 papers
Beyond Size and Class Balance: Alpha as a New Dataset Quality Metric for Deep Learning
Josiah Couch, Rima Arnaout, Ramy Arnaout
In deep learning, achieving high performance on image classification tasks requires diverse training sets. However, the current best practice$\unicode{x2013}$maximizing dataset siz…
Domain-guided data augmentation for deep learning on medical imaging
Chinmayee Athalye, Rima Arnaout
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. Here, we test whethe…
Label-free segmentation from cardiac ultrasound using self-supervised learning
Danielle L. Ferreira, Connor Lau, Zaynaf Salaymang +1
Segmentation and measurement of cardiac chambers is critical in cardiac ultrasound but is laborious and poorly reproducible. Neural networks can assist, but supervised approaches r…
Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions
Rima Arnaout, Lara Curran, Erin Chinn +2
Prenatal diagnosis of tetralogy of Fallot (TOF) and hypoplastic left heart syndrome (HLHS), two serious congenital heart defects, improves outcomes and can in some cases facilitate…
Fast and accurate classification of echocardiograms using deep learning
Ali Madani, Ramy Arnaout, Mohammad Mofrad +1
Echocardiography is essential to modern cardiology. However, human interpretation limits high throughput analysis, limiting echocardiography from reaching its full clinical and res…