All Competitors

Diffusion model for multichannel fluorescent cell microscopy, generating morphologically plausible images aligned to OpenPhenom phenotypic embeddings.

Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.

Breast ultrasound generative foundation model that synthesizes conditioned images to train screening, diagnosis, and prognosis models.

Cell Painting generative model encoding lab, batch, and well position as causal variables, predicting mechanism and target for unseen compounds.

Latent diffusion model that paints high-resolution Cell Painting images of cells responding to a chemical compound or an over-expressed gene.

Mixed-modal biomedical assistant that generates medical images as well as text, covering radiology and histopathology visual chat and captioning.

Text-guided medical image synthesis across OCT, fundus, X-ray, CT and MRI. Synthetic data lifts downstream clinical tasks by 12-17%.

Cell Painting image generation conditioned on a control well image and a compound's structure, covering cell lines and chemicals never trained on.

Protein subcellular localization from sequence, returning both a text label and a synthetic fluorescence image of the protein inside a given nucleus.

Text-conditioned latent diffusion model that generates synthetic chest X-rays from free-form radiology prompts by adapting Stable Diffusion.