Iván Hidalgo-Cenalmor

PhenoMe provides an open-source framework for analyzing microscopy images using foundation model-derived embeddings and experimental metadata to enable phenotyping and data exploration.

NucleiSky registers microscopy images across scales and modalities by aligning nuclei constellations, achieving accurate localization with minimal landmarks.

This study uses fast label-free live imaging and deep learning to analyze cancer cell arrest on endothelial monolayers, finding CD44-HA interactions and flow dynamics are key factors.

FeatureForest uses foundation model embeddings with random forests to enable fast, accurate segmentation of complex biological images with minimal labeling.

BiaPy provides an open-source library for deep learning in bioimage analysis, offering tools for 2D/3D image data with multi-GPU support and customizable workflows.

This paper trains a diffusion model to generate realistic super-resolution microscopy images and shows its effectiveness in improving deconvolution performance with limited real data.