1 citations · 1 across the 9 of their papers we have counts for
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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…
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…
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…