8 citations
- University of BaselCH4 papers
- Imperial College LondonGB2 papers
- Medical University of ViennaAT2 papers
- Moorfields Eye HospitalGB2 papers
- Moorfields Eye Hospital NHS Foundation TrustGB2 papers
- NIHR Moorfields Biomedical Research CentreGB2 papers
- TUM KlinikumDE2 papers
- University College LondonGB2 papers
- University of SouthamptonGB2 papers
- CEA Paris-SaclayFR1 paper
- Center for Systems BiologyUS1 paper
- Cognitive Neuroimaging LabFR1 paper
5 papers
VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images
Jose Vargas Quiros, Bart Liefers, Karin van Garderen +4
We introduce VascX models, a comprehensive set of model ensembles for analyzing retinal vasculature from color fundus images (CFIs). Annotated CFIs were aggregated from public data…
The linear-mixing approximation in silica-water mixtures at planetary conditions
Valiantsin Darafeyeu, Stephanie Rimle, Guglielmo Mazzola +1
The Linear Mixing Approximation (LMA) is often used in planetary models for calculating the equations of state (EoSs) of mixtures. A commonly assumed planetary composition is a mix…
3DTINC: Time-Equivariant Non-Contrastive Learning for Predicting Disease Progression from Longitudinal OCTs
Taha Emre, Arunava Chakravarty, Antoine Rivail +10
Self-supervised learning (SSL) has emerged as a powerful technique for improving the efficiency and effectiveness of deep learning models. Contrastive methods are a prominent famil…
Pretrained Deep 2.5D Models for Efficient Predictive Modeling from Retinal OCT
Taha Emre, Marzieh Oghbaie, Arunava Chakravarty +9
In the field of medical imaging, 3D deep learning models play a crucial role in building powerful predictive models of disease progression. However, the size of these models presen…
Biases and Variability from Costly Bayesian Inference
Arthur Prat-Carrabin, Florent Meyniel, Misha Tsodyks +1
When humans infer underlying probabilities from stochastic observations, they exhibit biases and variability that cannot be explained on the basis of sound, Bayesian manipulations…