5 papers
MRI-based Deep Radiomic Phenotyping of Neuromuscular Disorders: A Topology-driven Characterization
Martyna Żur, Łukasz Piórecki, Marek Socha +5
Quantitative assessment of muscle MRI is crucial for monitoring neuromuscular disorders (NMD). This study introduces an automated radiomic phenotyping framework based on original f…
Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT
Anna Mrukwa, Marek Socha, Aleksandra Suwalska +9
Background Lung cancer remains the deadliest cancer worldwide because it is often diagnosed too late. Effective treatment depends on detection at an early screening stage. However,…
GRAP-MOT: Unsupervised Graph-based Position Weighted Person Multi-camera Multi-object Tracking in a Highly Congested Space
Marek Socha, Michał Marczyk, Aleksander Kempski +4
GRAP-MOT is a new approach for solving the person MOT problem dedicated to videos of closed areas with overlapping multi-camera views, where person occlusion frequently occurs. Our…
Combining low-dose CT-based radiomics and metabolomics for early lung cancer screening support
Joanna Zyla, Michal Marczyk, Wojciech Prazuch +9
Due to its predominantly asymptomatic or mildly symptomatic progression, lung cancer is often diagnosed in advanced stages, resulting in poorer survival rates for patients. As with…
BRONCO: Automated modelling of the bronchovascular bundle using the Computed Tomography Images
Wojciech Prażuch, Marek Socha, Anna Mrukwa +9
Segmentation of the bronchovascular bundle within the lung parenchyma is a key step for the proper analysis and planning of many pulmonary diseases. It might also be considered the…