All Competitors

Segment Anything Model finetuned on diverse medical images, giving a reusable promptable checkpoint for interactive and automatic image segmentation.

3D vision foundation model for computed tomography, contrastively pretrained on 148,000 scans for segmentation, triage, retrieval, and concept search.

Cryo-EM reconstruction with neural radiance fields in Euclidean 3D space, separating conformational motion from compositional assembly states.

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

Multimodal vision-text foundation model for brain CT and MRI, pretrained on roughly 10 million image-report pairs to act as a clinical copilot.

Conditional diffusion model with cross-attention that synthesizes subject-specific 3D intrinsic connectivity networks from resting-state fMRI.

Tumor microenvironment segmentation on H&E slides, labeling 13 tissue and cell components from a single model in semantic or panoptic form.

Spike inference from calcium imaging traces, driven by a multistate GCaMP kinetic model that generates the synthetic data its decoders are trained on.

Cryo-ET particle picking model that localizes and classifies multiple protein complexes in a tomogram with a single 3D U-Net forward pass.

Variational autoencoder that learns interpretable representations of protein subtomograms from cryo-ET, trained on 5.8 million synthetic particles.

Cryo-ET segmentation framework adapting SAM2 to vesicles and membrane-bound compartments in tomograms and 2D micrographs, zero-shot or fine-tuned.

Instance segmentation for nervous-system tissue, trained only on biophysical simulations and applied to real brain, spinal cord and nerve sections.

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.