All Competitors

Spatial transcriptomics foundation model pairing gene expression with H&E histology for spatial domain discovery and clinical outcome prediction.

Diffusion-transformer pathology model embedding H&E histology, RNA profiles, and clinical text in a latent space for zero-shot cross-modal synthesis.

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.

Multimodal foundation model integrating spatial transcriptomics, H&E histopathology, and pathway scores for single-cell niche discovery.

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.

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

Renal pathology foundation model self-supervised on a million kidney biopsy tiles spanning glomeruli, interstitium, and surrounding structures.

Renal pathology foundation model whose learning unit is a whole detected glomerulus, pretrained on over a million of them across four biopsy stains.

Virtual staining from H&E histology to immunohistochemistry, using a Schrödinger bridge trained on unpaired slides with a topology-preserving term.

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

Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.

Histopathology tissue detection model that segments whole-slide thumbnails in a single SAM2 forward pass, replacing patch-wise slide preprocessing.

Pan-cancer model predicting spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.

Nuclei instance segmentation on H&E whole-slide images, predicting each nucleus as a star-convex polygon with no watershed or NMS separation step.

Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.

Multimodal foundation model that embeds histology, transcriptomics, and clinical records in one space for patient stratification and target discovery.

Pathology foundation model that aligns whole-slide images with genomic, epigenetic, and transcriptomic data for patient-level tumor representations.

Tissue imaging foundation model pretrained on matched H&E histology and spatial proteomics for cross-modal inference and zero-shot retrieval.

Cross-modal translator turning routine H&E slides into virtual 21-channel multiplex immunofluorescence maps of the tumor microenvironment.

Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.

Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.

Digital hematopathology foundation model unifying blood-cell detection, classification, segmentation, and visual question answering.