51 citations · 58 across the 10 of their papers we have counts for
11 papers
VIDS-Seg: Towards Reliable Uncertainty Quantification in Pediatric Cardiac Ultrasound Segmentation
Paul Fischer, Ece Ozkan
Reliable clinical deployment of machine learning requires models that know when they are likely to fail, particularly for subgroups underrepresented in training data. A common case…
Temporal Representation Learning for Real-Time Ultrasound Analysis
Yves Stebler, Thomas M. Sutter, Ece Ozkan +1
Ultrasound (US) imaging is a critical tool in medical diagnostics, offering real-time visualization of physiological processes. One of its major advantages is its ability to captur…
From Slices to Structures: Unsupervised 3D Reconstruction of Female Pelvic Anatomy from Freehand Transvaginal Ultrasound
Max Krähenmann, Sergio Tascon-Morales, Fabian Laumer +2
Volumetric ultrasound has the potential to significantly improve diagnostic accuracy and clinical decision-making, yet its widespread adoption remains limited by dependence on spec…
Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach
Lucas Erlacher, Samuel Ruipérez-Campillo, Holger Michel +4
Pulmonary hypertension (PH) in newborns is a critical condition characterized by elevated pressure in the pulmonary arteries, leading to right ventricular strain and heart failure.…
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms
Ece Ozkan, Thomas M. Sutter, Yurong Hu +2
Early detection of cardiac dysfunction through routine screening is vital for diagnosing cardiovascular diseases. An important metric of cardiac function is the left ventricular ej…
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis
Ričards Marcinkevičs, Patricia Reis Wolfertstetter, Ugne Klimiene +9
Appendicitis is among the most frequent reasons for pediatric abdominal surgeries. Previous decision support systems for appendicitis have focused on clinical, laboratory, scoring,…
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Many modern research fields increasingly rely on collecting and analysing massive, often unstructured, and unwieldy datasets. Consequently, there is growing interest in machine lea…
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods
Ričards Marcinkevičs, Ece Ozkan, Julia E. Vogt
Deep neural networks for image-based screening and computer-aided diagnosis have achieved expert-level performance on various medical imaging modalities, including chest radiograph…
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms
Hanna Ragnarsdottir, Laura Manduchi, Holger Michel +4
Pulmonary hypertension (PH) in newborns and infants is a complex condition associated with several pulmonary, cardiac, and systemic diseases contributing to morbidity and mortality…
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Alvaro Gomariz, Weiye Li, Ece Ozkan +2
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.,…
Herding Generalizes Diverse M -Best Solutions
Ece Ozkan, Gemma Roig, Orcun Goksel +1
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 determini…