80 citations · 100 across the 7 of their papers we have counts for
8 papers
A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model
Matej Gazda, Jakub Gazda, Juraj Gazda +1
Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared correspondence. Among released cardiac shape resourc…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
Large-scale modality-invariant foundation models for brain MRI analysis: Application to lesion segmentation
Petros Koutsouvelis, Matej Gazda, Leroy Volmer +5
The field of computer vision is undergoing a paradigm shift toward large-scale foundation model pre-training via self-supervised learning (SSL). Leveraging large volumes of unlabel…
Generative Adversarial Networks in Ultrasound Imaging: Extending Field of View Beyond Conventional Limits
Matej Gazda, Samuel Kadoury, Jakub Gazda +1
Transthoracic Echocardiography (TTE) is a fundamental, non-invasive diagnostic tool in cardiovascular medicine, enabling detailed visualization of cardiac structures crucial for di…
End-to-end Deformable Attention Graph Neural Network for Single-view Liver Mesh Reconstruction
Matej Gazda, Peter Drotar, Liset Vazquez Romaguera +1
Intensity modulated radiotherapy (IMRT) is one of the most common modalities for treating cancer patients. One of the biggest challenges is precise treatment delivery that accounts…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Self-supervised deep convolutional neural network for chest X-ray classification
Matej Gazda, Jakub Gazda, Jan Plavka +1
Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are almost always used in the…
Towards annotation-efficient segmentation via image-to-image translation
Eugene Vorontsov, Pavlo Molchanov, Christopher Beckham +2
Often in medical imaging, it is prohibitively challenging to produce enough boundary annotations to train deep neural networks for accurate tumor segmentation. We propose the use o…