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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.
Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment
Obstetric ultrasound image quality is crucial for accurate diagnosis and monitoring of fetal health.
Shortcut Learning in Medical Image Segmentation
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 set.
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.
Removing confounding information from fetal ultrasound images
In order to utilize the vast amounts of data available in these databases, we develop and validate a series of methods for minimizing the confounding effects of embedded text and calipers on deep learning algorithms designed for ultrasound, using standard plane classification as a test case.
