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Found 8 papers, 3 papers with code
Multi-domain improves out-of-distribution and data-limited scenarios for medical image analysis
We refer to this approach as multi-domain model and compare its performance to that of specialized models.
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases.
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries.
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets.
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
In addition, we compare several intra- and post-processing approaches applied to debiasing deep chest X-ray classifiers.
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms
To the best of our knowledge, this is the first work for an automated assessment of PH in newborns using echocardiograms.
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e. g., due to breathing.
Herding Generalizes Diverse M -Best Solutions
We show that the algorithm to extract diverse M -solutions from a Conditional Random Field (called divMbest [1]) takes exactly the form of a Herding procedure [2], i. e. a deterministic dynamical system that produces a sequence of hypotheses that respect a set of observed moment constraints.
