1 citations · 2 across the 14 of their papers we have counts for
17 papers
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…
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…
Inductive Domain Transfer In Misspecified Simulation-Based Inference
Ortal Senouf, Antoine Wehenkel, Cédric Vincent-Cuaz +2
Simulation-based inference (SBI) is a statistical inference approach for estimating latent parameters of a physical system when the likelihood is intractable but simulations are av…
CM-UNet: A Self-Supervised Learning-Based Model for Coronary Artery Segmentation in X-Ray Angiography
Camille Challier, Xiaowu Sun, Thabo Mahendiran +8
Accurate segmentation of coronary arteries remains a significant challenge in clinical practice, hindering the ability to effectively diagnose and manage coronary artery disease. T…
Flow based approach for Dynamic Temporal Causal models with non-Gaussian or Heteroscedastic Noises
Abdellah Rahmani, Pascal Frossard
Understanding causal relationships in multivariate time series is crucial in many scenarios, such as those dealing with financial or neurological data. Many such time series exhibi…
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…
Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology
Boqi Chen, Cédric Vincent-Cuaz, Lydia A. Schoenpflug +12
Vision foundation models (FMs) are accelerating the development of digital pathology algorithms and transforming biomedical research. These models learn, in a self-supervised manne…
DeFoG: Discrete Flow Matching for Graph Generation
Yiming Qin, Manuel Madeira, Dorina Thanou +1
Graph generative models are essential across diverse scientific domains by capturing complex distributions over relational data. Among them, graph diffusion models achieve superior…
Generative Modelling of Structurally Constrained Graphs
Manuel Madeira, Clement Vignac, Dorina Thanou +1
Graph diffusion models have emerged as state-of-the-art techniques in graph generation; yet, integrating domain knowledge into these models remains challenging. Domain knowledge is…
Sparse Training of Discrete Diffusion Models for Graph Generation
Yiming Qin, Clement Vignac, Pascal Frossard
Generative graph models struggle to scale due to the need to predict the existence or type of edges between all node pairs. To address the resulting quadratic complexity, existing…
Causal Temporal Regime Structure Learning
Abdellah Rahmani, Pascal Frossard
Understanding causal relationships in multivariate time series is essential for predicting and controlling dynamic systems in fields like economics, neuroscience, and climate scien…
Tertiary Lymphoid Structures Generation through Graph-based Diffusion
Manuel Madeira, Dorina Thanou, Pascal Frossard
Graph-based representation approaches have been proven to be successful in the analysis of biomedical data, due to their capability of capturing intricate dependencies between biol…