3 citations · 7 across the 10 of their papers we have counts for
6 papers · 1 filter
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Markus Ditlev Sjøgren Olsen, Jakob Ambsdorf, Manxi Lin +7
Congenital malformations of the brain are among the most common fetal abnormalities that impact fetal development. Previous anomaly detection methods on ultrasound images are based…
Incorporating Clinical Guidelines through Adapting Multi-modal Large Language Model for Prostate Cancer PI-RADS Scoring
Tiantian Zhang, Manxi Lin, Hongda Guo +4
The Prostate Imaging Reporting and Data System (PI-RADS) is pivotal in the diagnosis of clinically significant prostate cancer through MRI imaging. Current deep learning-based PI-R…
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…