bioimage-analysis

Our BioEngine preprint is out on bioRxiv. It turns the explosion of community AI models into something a biologist — or an AI agent — can actually run, on a laptop or an HPC cluster, no code required.

Today in AI for life science: an AI agent that drives ImageJ, the case for ’thinking microscopes’ that run their own experiments, and a foundation model that imagines how cells morph under perturbation.

A distributed platform for deploying and adapting bioimage AI models as agent-readable interfaces, with autoscaling and FAIR data access — the engine behind the BioImage Model Zoo test runs.

Control microscopes in plain language — LLM agents that turn scientific intent into imaging, segmentation, and analysis.

A universal deep-learning approach for accurate FISH spot detection across imaging-based spatial-omics datasets.

The classic ImageJ, reimagined to run entirely in the web browser — no installation, full plugin support, and AI assistance.

A community-driven, AI-powered assistant that helps researchers navigate bioimaging tools, databases and services — and autonomously runs analysis tasks from plain-language prompts. Published in Nature Methods.

A framework that makes deep-learning pipelines for microscopy flexible, shareable, and reproducible across computing environments.

We are delighted to welcome Jeremy, a Research Engineer with diverse expertise in computer vision, computational biology, and modelling. His arrival marks an exciting chapter in AICell Lab’s journey towards advancing data-driven life sciences.