3 citations · 9 across the 8 of their papers we have counts for
9 papers
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…
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Paraskevas Pegios, Manxi Lin, Nina Weng +6
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health. However, acquiring high-quality standard planes is difficult, influenced by the…
Shortcut Learning in Medical Image Segmentation
Manxi Lin, Nina Weng, Kamil Mikolaj +5
Shortcut learning is a phenomenon where machine learning models prioritize learning simple, potentially misleading cues from data that do not generalize well beyond the training se…
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…
Assessing the Interpretability of Programmatic Policies with Large Language Models
Zahra Bashir, Michael Bowling, Levi H. S. Lelis
Although the synthesis of programs encoding policies often carries the promise of interpretability, systematic evaluations were never performed to assess the interpretability of th…
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…
Removing confounding information from fetal ultrasound images
Kamil Mikolaj, Manxi Lin, Zahra Bashir +4
Confounding information in the form of text or markings embedded in medical images can severely affect the training of diagnostic deep learning algorithms. However, data collected…
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Manxi Lin, Aasa Feragen, Kamil Mikolaj +3
The quality of fetal ultrasound screening scans directly influences the precision of biometric measurements. However, acquiring high-quality scans is labor-intensive and highly rel…
DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation
Manxi Lin, Zahra Bashir, Martin Grønnebæk Tolsgaard +2
Curvilinear structure segmentation is important in medical imaging, quantifying structures such as vessels, airways, neurons, or organ boundaries in 2D slices. Segmentation via pix…