5 papers
Towards Reliable and Reproducible Fetal Brain Biometry: A Deep Learning Approach Using MRI
Francesca Maccarone, Marina Di Stefano, Giorgio Longari +7
Fetal brain biometry is essential for quantitative assessment of brain development, supporting gestational age estimation, developmental monitoring, and detection of abnormalities.…
Varifold Moment Invariants for Sustainable and Explainable Contour Feature Extraction
G. Longari, J. -C. Alvarez Paiva, A. B. Tumpach
We introduce Varifold Moments Invariants (VMI) as a unifying framework for many previously introduced Moment Invariants. These invariants are deeply related to other contour featur…
Geometric Learning of Canonical Parameterizations of -curves
Ioana Ciuclea, Giorgio Longari, Alice Barbara Tumpach
Most datasets encountered in computer vision and medical applications present symmetries that should be taken into account in classification tasks. A typical example is the symmetr…
Blending Concepts with Text-to-Image Diffusion Models
Lorenzo Olearo, Giorgio Longari, Alessandro Raganato +2
Diffusion models have dramatically advanced text-to-image generation in recent years, translating abstract concepts into high-fidelity images with remarkable ease. In this work, we…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…