1 citations · 1 across the 7 of their papers we have counts for
8 papers
Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification
Youssef Megahed, Salma I. Megahed, Robin Ducharme +4
Reliable interpretation of cardiac ultrasound images is essential for accurate clinical diagnosis and assessment. Self-supervised learning has shown promise in medical imaging by l…
Improved cystic hygroma detection from prenatal imaging using ultrasound-specific self-supervised representation learning
Youssef Megahed, Robin Ducharme, Inok Lee +4
Cystic hygroma is a high-risk prenatal ultrasound finding that portends high rates of chromosomal abnormalities, structural malformations, and adverse pregnancy outcomes. Automated…
Automated Classification of First-Trimester Fetal Heart Views Using Ultrasound-Specific Self-Supervised Learning
Youssef Megahed, Aylin Erman, Robin Ducharme +3
Congenital heart disease remains the most common congenital anomaly and a leading cause of neonatal morbidity and mortality. Although first-trimester fetal echocardiography offers…
Self-Supervised Ultrasound Representation Learning for Renal Anomaly Prediction in Prenatal Imaging
Youssef Megahed, Inok Lee, Robin Ducharme +4
Prenatal ultrasound is the cornerstone for detecting congenital anomalies of the kidneys and urinary tract, but diagnosis is limited by operator dependence and suboptimal imaging c…
Deep Learning Analysis of Prenatal Ultrasound for Identification of Ventriculomegaly
Youssef Megahed, Inok Lee, Robin Ducharme +7
The proposed study aimed to develop a deep learning model capable of detecting ventriculomegaly on prenatal ultrasound images. Ventriculomegaly is a prenatal condition characterize…
USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding
Youssef Megahed, Robin Ducharme, Aylin Erman +3
Ultrasound imaging is one of the most widely used diagnostic modalities, offering real-time, radiation-free assessment across diverse clinical domains. However, interpretation of u…
CAManim: Animating end-to-end network activation maps
Emily Kaczmarek, Olivier X. Miguel, Alexa C. Bowie +6
Deep neural networks have been widely adopted in numerous domains due to their high performance and accessibility to developers and application-specific end-users. Fundamental to i…
MetaCAM: Ensemble-Based Class Activation Map
Emily Kaczmarek, Olivier X. Miguel, Alexa C. Bowie +6
The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activ…