collaborators

6 papers

cs.CV2026

OREHAS: A fully automated deep-learning pipeline for volumetric endolymphatic hydrops quantification in MRI

Caterina Fuster-Barceló, Claudia Castrillón, Laura Rodrigo-Muñoz +4

We present OREHAS (Optimized Recognition & Evaluation of volumetric Hydrops in the Auditory System), the first fully automatic pipeline for volumetric quantification of endolymphat…

cs.CV2025

Multimodal Posterior Sampling-based Uncertainty in PD-L1 Segmentation from H&E Images

Roman Kinakh, Gonzalo R. Ríos-Muñoz, Arrate Muñoz-Barrutia

Accurate assessment of PD-L1 expression is critical for guiding immunotherapy, yet current immunohistochemistry (IHC) based methods are resource-intensive. We present nnUNet-B: a B…

q-bio.OT2025

MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis

Teresa Zulueta-Coarasa, Florian Jug, Aastha Mathur +24

Artificial Intelligence methods are powerful tools for biological image analysis and processing. High-quality annotated images are key to training and developing new methods, but a…

cs.CY2025

The impact of gamification on learning outcomes: experiences from a Biomedical Engineering course

Gonzalo R. Ríos-Muñoz, Caterina Fuster-Barcelo, Arrate Muñoz-Barrutia

This study examines the integration of digital tools in project-based learning within a Biomedical Engineering course to enhance collaboration, transparency, and assessment fairnes…

cs.LG2025

Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodology

Caterina Fuster-Barcelo, Gonzalo R. Rios-Munoz, Arrate Munoz-Barrutia

This study examines the integration of digital collaborative tools and structured peer evaluation in the Machine Learning for Health master's program, through the redesign of a Bio…

cs.CV2025

SAMJ: Fast Image Annotation on ImageJ/Fiji via Segment Anything Model

Carlos Garcia-Lopez-de-Haro, Caterina Fuster-Barcelo, Curtis T. Rueden +9

Mask annotation remains a significant bottleneck in AI-driven biomedical image analysis due to its labor-intensive nature. To address this challenge, we introduce SAMJ, a user-frie…