Estibaliz Gómez‐de‐Mariscal

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.

This paper evaluates how reducing image resolution affects deep learning performance in bioimage segmentation, finding that lower resolution can improve accuracy or cause minimal degradation.

The paper discusses AI4Life's Open Calls and Public Challenges, highlighting the state of bioimage analysis, tool availability, and the need for better data and infrastructure.

PhotoFiTT quantifies phototoxicity in live-cell microscopy by analyzing mitotic timing, cell size, and activity, showing near-UV light causes significant mitotic delays at low doses.

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

The paper introduces GAT, a tool for automated analysis of enteric neurons in gut tissue images, improving accuracy and throughput over manual methods.

This paper introduces enhancements to deepImageJ, a Fiji/ImageJ plugin, by integrating the Java Deep Learning Library (JDLL) to support multiple deep learning frameworks, enabling complex bioimage analysis tasks.

CellTracksColab is a platform for compiling, analyzing, and exploring tracking data from biological movies, enabling holistic dataset analysis and identification of behavioral patterns.

The BioImage Model Zoo provides a community standard, infrastructure, and open repository for sharing, testing, and deploying pre-trained deep learning microscopy models across multiple bioimage analysis tools.

The paper proposes a method to adjust p-values based on sample size to better detect differences in large datasets and reduce uncertainty in statistical decisions.