1 citations · 1 across the 9 of their papers we have counts for
9 papers
Beyond Point Estimates for Glaucoma Visual Field Forecasting with Diffusion Models
Marta Colmenar Herrera, Pablo Márquez Neila, Şerife Seda Kucur Ergünay +2
Forecasting visual fields (VFs) is critical for personalized monitoring and treatment planning in glaucoma. This is inherently uncertain due to heterogeneous disease progression an…
Inferring geometry and material properties from Mueller matrices with machine learning
Lars Doorenbos, C. H. Lucas Patty, Raphael Sznitman +1
Mueller matrices (MMs) encode information on geometry and material properties, but recovering both simultaneously is an ill-posed problem. We explore whether MMs contain sufficient…
SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation
Javier Gamazo Tejero, Moritz Schmid, Pablo Márquez Neila +3
This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models lik…
Non-Linear Outlier Synthesis for Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
The reliability of supervised classifiers is severely hampered by their limitations in dealing with unexpected inputs, leading to great interest in out-of-distribution (OOD) detect…
Targeted Visual Prompting for Medical Visual Question Answering
Sergio Tascon-Morales, Pablo Márquez-Neila, Raphael Sznitman
With growing interest in recent years, medical visual question answering (Med-VQA) has rapidly evolved, with multimodal large language models (MLLMs) emerging as an alternative to…
Learning Non-Linear Invariants for Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
The inability of deep learning models to handle data drawn from unseen distributions has sparked much interest in unsupervised out-of-distribution (U-OOD) detection, as it is cruci…
Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
Lars Doorenbos, Eva Sextl, Kevin Heng +6
Modern spectroscopic surveys can only target a small fraction of the vast amount of photometrically cataloged sources in wide-field surveys. Here, we report the development of a ge…
Iterative Deployment Exposure for Unsupervised Out-of-Distribution Detection
Lars Doorenbos, Raphael Sznitman, Pablo Márquez-Neila
Deep learning models are vulnerable to performance degradation when encountering out-of-distribution (OOD) images, potentially leading to misdiagnoses and compromised patient care.…
Masked Image Modelling for retinal OCT understanding
Theodoros Pissas, Pablo Márquez-Neila, Sebastian Wolf +2
This work explores the effectiveness of masked image modelling for learning representations of retinal OCT images. To this end, we leverage Masked Autoencoders (MAE), a simple and…