8 papers · 1 filter
RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities
Vasiliki Rizou, Pascal Frossard, Dorina Thanou
To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interacti…
Causality-Driven Disentangled Representation Learning in Multiplex Graphs
Saba Nasiri, Selin Aviyente, Dorina Thanou
Learning representations from multiplex graphs, i.e., multi-layer networks where nodes interact through multiple relation types, is challenging due to the entanglement of shared (c…
ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images
Anand Choudhary, Xiaowu Sun, Thabo Mahendiran +8
Invasive Coronary Angiography (ICA) is the clinical gold standard for the assessment of coronary artery disease. However, its interpretation remains subjective and prone to intra-…
Generating Directed Graphs with Dual Attention and Asymmetric Encoding
Alba Carballo-Castro, Manuel Madeira, Yiming Qin +2
Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, and visual understanding. Gen…
Physics-informed self-supervised learning for predictive modeling of coronary artery digital twins
Xiaowu Sun, Thabo Mahendiran, Ortal Senouf +7
Cardiovascular disease is the leading global cause of mortality, with coronary artery disease (CAD) as its most prevalent form, necessitating early risk prediction. While 3D corona…
Continuous Simplicial Neural Networks
Aref Einizade, Dorina Thanou, Fragkiskos D. Malliaros +1
Simplicial complexes provide a powerful framework for modeling higher-order interactions in structured data, making them particularly suitable for applications such as trajectory p…