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Found 10 papers, 1 papers with code
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.
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.
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.
Was that so hard? Estimating human classification difficulty
In this paper, we introduce methods for estimating how hard it is for a doctor to diagnose a case represented by a medical image, both when ground truth difficulties are available for training, and when they are not.
Multi-modal data generation with a deep metric variational autoencoder
We present a deep metric variational autoencoder for multi-modal data generation.
Complex-valued neural networks for machine learning on non-stationary physical data
While it has been shown that phase content can be restored in deep neural networks, we show how including phase information in feature maps improves both training and inference from deterministic physical data.
