All Competitors

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

Multimodal foundation model pretrained on 1.76 billion paired histology-spatial transcriptomics spots, linking whole-slide images to spatial molecular programs.

Multimodal tumor foundation model trained on paired H&E histology and spatial transcriptomics to infer whole-transcriptome and tumor-microenvironment signal from routine H&E alone.

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

A diffusion transformer that synthesizes H&E histopathology image patches conditioned jointly on spatial transcriptomics gene expression and morphological embeddings.

A multi-organ foundation model aligning histology image features with spatial-transcriptomics gene expression across 13 organs for zero-shot virtual ST and survival prediction.

Latent flow-matching foundation model that predicts pan-cancer spatially-resolved single-cell gene expression directly from routine H&E histology slides.

A foundation model that predicts spatial transcriptomics and proteomics directly from routine H&E whole-slide images using a vision transformer aligned with a language model.

A Schrödinger-bridge diffusion model for virtual multiplex staining that translates standard H&E histology into multiplex immunohistochemistry images.

A generative diffusion-transformer foundation model that embeds H&E histology, RNA profiles, and clinical text in a shared latent space for zero-shot cross-modal synthesis.

A hierarchical multimodal foundation model integrating spatial transcriptomics and H&E histology for biological discovery and platform-agnostic clinical prediction.

1.1B-parameter histopathology foundation model trained on public data with a JEDI (JEPA+DINO) dual-stage strategy, reaching state-of-the-art on THUNDER, HEST, and PathoROB.

Foundation-model-guided virtual staining that generates four IHC markers (HER2, Ki67, ER, PR) from H&E using a single SPADE-UNet conditioned on a frozen UNI encoder.

A self-supervised, cell-centric pretraining strategy that distills morphology and microenvironment views of each cell into a unified embedding for virtual spatial omics from microscopy.

Cross-species, multimodal foundation model of immunology and inflammation that harmonizes transcriptomics and histology into unified patient-level representations.

A pan-cancer generative model that predicts spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.

Bidirectional generative framework linking single-cell nuclear morphology and gene expression, built on a morphology foundation model trained on 21M+ segmented nuclei.

Generative foundation model that enhances spatial transcriptomics by conditioning on H&E histology, scRNA-seq references, and spatial co-expression priors.

Frontier-scale digital pathology foundation models from PathAI, spanning a compact 22M-parameter variant and a 1.1B-parameter flagship trained on 551K whole-slide images.

A generalizable computational-pathology foundation model trained on ~190M histopathology patches via unified knowledge distillation from UNI, Phikon, and CONCH.

A medical vision-language model that accepts visual-referring multimodal input and produces pixel-grounded multimodal output, jointly answering and segmenting medical images.

A 4.2B-parameter lightweight biomedical vision-language assistant built on Phi-2 that outperforms larger LLaVA-Med models on medical visual question answering.

Medical multimodal LLM (2B and 8B) trained for generalizable, step-by-step clinical reasoning via Mentor-Intern Collaborative Search.

A generalist medical multimodal LLM built on Qwen2.5-VL for unified medical image understanding, visual question answering, report generation, and clinical reasoning across 12+ imaging modalities.