12 papers
Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
Gbègninougbo Aurel Davy Tchokponhoue, Sevda ÃÄüt, Ali Idri +2
Pathology foundation models (PFMs) offer generalizable representations for whole-slide image (WSI) analysis, yet their clinical adoption remains limited. Specifically, their predic…
Graph Signal Separation with Learnable Spectral Filters
Keivan Faghih Niresi, Dorina Thanou, Olga Fink
Separating multiple graph signals from a single observed mixture is an inherently ill-posed problem that traditionally relies on restrictive and handcrafted priors. This letter add…
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-…
GrapHist: Graph Self-Supervised Learning for Histopathology
Sevda ÃÄüt, Cédric Vincent-Cuaz, Natalia Dubljevic +4
Self-supervised vision models have achieved notable success in digital pathology. However, their domain-agnostic transformer architectures are not originally designed to account fo…