microscopy

A self-driving cell-biology lab where AI agents plan, run and adapt live-cell experiments across a cluster of instruments.

Wei Ouyang and Hanzhao Zhang wrote the ‘Large Language Models and AI Agents’ chapter for AI in Microscopy: A BioImaging Guide (HHMI Janelia) — a tour from deep learning to autonomous, agent-driven bioimage analysis.

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.

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

A collaborative community roadmap surveying how deep learning is applied across microscopy — image restoration, detection, segmentation, classification, tracking, and multimodal fusion — with its possibilities and limitations.

An open, highly configurable, general-purpose automated microscopy platform for low-cost, high-throughput diagnostics — including malaria screening, sickle cell anemia, and antibiotic resistance — designed for use in low-resource settings.

A domain adaptation of video diffusion models that generates realistic time-lapse microscopy videos of dividing HeLa cells — conditioned on phenotype measurements or an initial frame — capturing mitosis and migration for in-silico hypothesis testing and data augmentation.

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

Building an AI-powered self-driving microscopy system for studying cellular response