1 citations · 2 across the 14 of their papers we have counts for
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spora: A Unified Multimodal Dataset and Platform for Spatial Proteomics
Benedikt von Querfurth, Johann Wenckstern, Eeshaan Jain +9
Spatial proteomics (SP) captures the molecular composition and spatial organization of tissues at single-cell resolution, opening a direct view of the cellular ecosystems that driv…
DiPhon: Diffusion on Graphons for Scalable Graph Generation
Sergio Rozada, Yiming Qin, Manuel Madeira +2
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an op…
Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning
Gbègninougbo Aurel Davy Tchokponhoue, Sevda Öğüt, 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…
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…
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 Öğüt, 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…