All Competitors

Hierarchy-aware self-supervised model for single-cell microscopy that preserves morphological structure suppressed by imaging-modality confounders.

Brain MRI morphometry model that estimates cortical thickness, surface area, and volume in milliseconds instead of hours.

Vision-guided model that builds virtual 3D organoid surrogates from brightfield microscopy to predict chemical perturbation responses without omics.

Self-supervised 3D masked autoencoder for volumetric fluorescence microscopy, aligned to ESM2 embeddings to predict protein localization.

Multi-sequence spine MRI foundation model with DINOv3 encoders, supporting condition classification, pathology localization, and report generation.

Pathology vision foundation model adapting DINOv3 self-supervised learning to whole-slide histopathology across many magnifications and scales.

Tri-modal foundation model unifying histology images, spatial transcriptomics, and language for zero-shot pathology and spatial biology reasoning.

Genetically aligned foundation model for blood smear cytology that links single-cell morphology to the chromosomal aberrations behind AML and APL.

Lung pathology foundation model adapted from Virchow2 on whole-slide images, validated across 32 tasks spanning the lung diagnostic workflow.

Domain-specific foundation model for zero-shot plant root image segmentation, built on a MobileSAM backbone and trained across nine root datasets.

Multi-organ foundation model aligning histology images with spatial-transcriptomics profiles for zero-shot expression and survival prediction.

Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.

Pathology foundation model that infers spatial transcriptomics and proteomics directly from routine H&E whole-slide images, with no spatial assay.

Genomic foundation model that learns DNA representations by predicting masked regions in latent space rather than reconstructing raw nucleotides.

Whole-cell segmentation model for spatial transcriptomics that fuses DAPI nuclear images with RNA transcript density to recover true cell boundaries.

Gastrointestinal histopathology foundation model pretrained on 353 million multi-scale patches from 210,000 H&E whole-slide images of GI tissue.

Histopathology foundation model with 1.1B parameters, trained entirely on public data using JEDI, a dual-stage strategy combining JEPA and DINO.

Virtual staining model that generates four IHC markers, HER2, Ki67, ER, and PR, from H&E using a generator conditioned on a frozen UNI encoder.

Vision transformer trained with DINO self-supervision to segment micronuclei in DNA-stained fluorescence images across cell lines and microscopes.

Cell-centric microscopy foundation model that distills morphology and microenvironment views into a unified embedding for virtual spatial omics.

Vision-omics finetuning that aligns pathology foundation models with spatial transcriptomics so morphology features predict local gene expression.

Vision foundation model for the tree of life, scaling BioCLIP 2 to a ViT-H/14 backbone and more organism images for zero-shot species classification.

Joint-embedding predictive foundation model for echocardiography, pretrained on 18M cardiac ultrasound videos for artifact-robust representations.

Vision-language DNA model that renders genomic sequence as visual layouts, reading regions up to 450,000 bases with about 20x better token efficiency.