All Competitors

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

Tilt interpolation for cryo-electron tomography, synthesizing intermediate projections to improve angular sampling without extra electron dose.

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.

Cell type annotation from multiplexed tissue images, using a pretrained Vision Transformer ensemble that runs on new panels without fine-tuning.

Latent diffusion model generating Cell Painting images for a compound from its predicted bioactivity profile, reaching unfamiliar chemical matter.

Cryo-EM heterogeneous reconstruction that models particles as one of K neural fields, resolving compositional and conformational states ab initio.

Biomedical multimodal LLM that answers questions about medical images and returns pixel-level segmentation masks, using a mixture-of-experts design.

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

Bilingual Arabic-English medical multimodal model built on Llama 3.1 for radiology, CT, and histology image understanding and question answering.

Ligand identification in cryoEM and X-ray density maps, classifying a density blob into one of 219 ligand groups from its 3D point cloud shape.

Vision transformers trained on Human Protein Atlas fluorescence microscopy for subcellular protein localization and cell morphology representation.

Tissue-aware foundation model that restores brain MRI quality across motion correction, super-resolution, denoising, and harmonization.

Self-supervised vision foundation model for structural brain MRI, providing a reusable encoder for brain age, survival, and image classification.

Spatiotemporal vision transformer that turns a resting-state fMRI scan into 4D brain network maps, supervised by windowed ICA components.

3D blood vessel segmentation across CT, MRI, light-sheet microscopy and OCTA volumes, generalizing zero-shot to imaging domains absent from training.

Multi-scale latent diffusion model for histopathology that synthesizes tissue patches at any magnification and composes them into 4096-pixel images.

Radiology CT framework pairing lesion-driven contrastive slice embeddings with attention pooling to predict clinical endpoints without fine-tuning.

Renal pathology segmentation model resolving 14 glomerular tissue, cell, and lesion classes in human and mouse whole-slide images.

Ultrasound foundation model pretrained by federated learning across 16 institutions, transferring to disease diagnosis and lesion segmentation.

Promptable ultrasound image segmentation foundation model, a SAM adaptation trained on US-43d, the largest public ultrasound segmentation corpus.

Conditional denoising diffusion model that synthesizes chest radiographs from patient demographics and echocardiographic ventricular measurements.

Biomedical imaging foundation model that segments, detects, and recognizes structures across nine modalities from natural language prompts.