1 citations · 1 across the 5 of their papers we have counts for
6 papers
Weight Space Correlation Analysis: Quantifying Feature Utilization in Deep Learning Models
Chun Kit Wong, Paraskevas Pegios, Nina Weng +4
Deep learning models in medical imaging are susceptible to shortcut learning, relying on confounding metadata (e.g., scanner model) that is often encoded in image embeddings. The c…
Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasound
Chun Kit Wong, Anders N. Christensen, Cosmin I. Bercea +3
Reliable out-of-distribution (OOD) detection is important for safe deployment of deep learning models in fetal ultrasound amidst heterogeneous image characteristics and clinical se…
Determining Fetal Orientations From Blind Sweep Ultrasound Video
Jakub Maciej Wiśniewski, Anders Nymark Christensen, Mary Le Ngo +2
Cognitive demands of fetal ultrasound examinations pose unique challenges among clinicians. With the goal of providing an assistive tool, we developed an automated pipeline for pre…
Testing of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Chun Kit Wong, Mary Ngo, Manxi Lin +6
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. To address this, we introduce a generic fram…
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
Manxi Lin, Jakob Ambsdorf, Emilie Pi Fogtmann Sejer +9
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements. Working in fetal ultrasound, where ranking is challenging an…
An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
Chun Kit Wong, Manxi Lin, Alberto Raheli +5
Examination of the umbilical artery with Doppler ultrasonography is performed to investigate blood supply to the fetus through the umbilical cord, which is vital for the monitoring…