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Found 7 papers, 1 papers with code
Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models
Our experiments on a real-world clinical dataset show the potential of using unsupervised methods for fetal brain anomaly detection.
Deployment of Deep Learning Model in Real World Clinical Setting: A Case Study in Obstetric Ultrasound
Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited.
Learning semantic image quality for fetal ultrasound from noisy ranking annotation
We introduce the notion of semantic image quality for applications where image quality relies on semantic requirements.
An Automatic Guidance and Quality Assessment System for Doppler Imaging of Umbilical Artery
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 of fetal health.
I saw, I conceived, I concluded: Progressive Concepts as Bottlenecks
Concept bottleneck models (CBMs) include a bottleneck of human-interpretable concepts providing explainability and intervention during inference by correcting the predicted, intermediate concepts.
Generalisability of fetal ultrasound deep learning models to low-resource imaging settings in five African countries
This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for usability of AI in countries with less resources.
DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation
We conduct experiments on a challenging multi-class ultrasound scan segmentation dataset as well as a well-known retinal imaging dataset.
