All Competitors

Spatial transcriptomics prediction from histology using conditional neural fields to reconstruct continuous gene expression fields.

Histopathology-to-molecular alignment model that queries H&E slides with gene-set signatures to predict pathway activity without sequencing.

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

Multimodal foundation model pretrained on 1.76B histology and spatial transcriptomics spots, inferring molecular state from whole-slide images.

Tumor foundation model that infers whole-transcriptome and microenvironment signal from H&E slides, pretrained on paired spatial transcriptomics.

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

Diffusion transformer for virtual tissue synthesis, generating H&E histopathology patches conditioned on spatial gene expression and morphology.

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

Virtual spatial transcriptomics foundation model predicting pan-cancer, spatially-resolved single-cell gene expression from H&E histology slides.

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

Schrödinger-bridge diffusion model for virtual multiplex staining, translating routine H&E histology into multiplex immunohistochemistry images.

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

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

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.

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.

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

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

Bidirectional generative framework linking nuclear morphology and gene expression, built on a morphology model trained on 21 million segmented nuclei.

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

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.

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