1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
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…
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…