Aitor González-Marfil

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

DINOSim detects and segments objects in microscopy images without annotated data, achieving performance comparable to supervised methods using a pretrained DINOv2 encoder and k-nearest neighbors refinement.

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.

The paper develops self-supervised Vision Transformers for predicting fluorescence images from label-free transmitted light microscopy inputs, using the LightMyCells challenge as a benchmark.