17 citations · 34 across the 6 of their papers we have counts for
8 papers
Multi-Domain Brain Vessel Segmentation Through Feature Disentanglement
Francesco Galati, Daniele Falcetta, Rosa Cortese +3
The intricate morphology of brain vessels poses significant challenges for automatic segmentation models, which usually focus on a single imaging modality. However, accurately trea…
ImageSet2Text: Describing Sets of Images through Text
Piera Riccio, Francesco Galati, Kajetan Schweighofer +2
In the era of large-scale visual data, understanding collections of images is a challenging yet important task. To this end, we introduce ImageSet2Text, a novel method to automatic…
HyperMM : Robust Multimodal Learning with Varying-sized Inputs
Hava Chaptoukaev, Vincenzo Marcianó, Francesco Galati +1
Combining multiple modalities carrying complementary information through multimodal learning (MML) has shown considerable benefits for diagnosing multiple pathologies. However, the…
The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiography
Kaiyuan Yang, Fabio Musio, Yihui Ma +112
The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to influence the risk, severity, an…
A2V: A Semi-Supervised Domain Adaptation Framework for Brain Vessel Segmentation via Two-Phase Training Angiography-to-Venography Translation
Francesco Galati, Daniele Falcetta, Rosa Cortese +4
We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality…
Binary domain generalization for sparsifying binary neural networks
Riccardo Schiavone, Francesco Galati, Maria A. Zuluaga
Binary neural networks (BNNs) are an attractive solution for developing and deploying deep neural network (DNN)-based applications in resource constrained devices. Despite their su…
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…
Efficient Model Monitoring for Quality Control in Cardiac Image Segmentation
Francesco Galati, Maria A. Zuluaga
Deep learning methods have reached state-of-the-art performance in cardiac image segmentation. Currently, the main bottleneck towards their effective translation into clinics requi…