All Competitors

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

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

Genetically aligned foundation model for blood smear cytology that links single white-blood-cell morphology to chromosomal aberrations and mutations for AML/APL diagnosis.

A subspecialty lung-pathology foundation model, fine-tuned from Virchow2 and prospectively validated across 32 clinical tasks spanning the lung diagnostic workflow.

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

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.

A self-supervised DINOv3-based foundation model for brain MRI, pretrained on ~6.6M unlabeled axial slices and transferable to diverse neuroimaging tasks.

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 genomic foundation model that learns DNA representations through joint-embedding prediction in latent space rather than nucleotide reconstruction.

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

Subspecialty-specific computational pathology foundation model pretrained on 353 million multi-scale patches from 210,000 H&E slides for gastrointestinal pathology, achieving SOTA on 32 of 33 systematic downstream tasks.

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 DINO-pretrained vision transformer for accurate, robust segmentation of micronuclei in DNA-stained fluorescence microscopy across cell lines and instruments.

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.

An OCR-inspired vision-language model that renders DNA as visual layouts to analyze long genomic sequences with far fewer tokens than sequential tokenizers.

A Joint-Embedding Predictive foundation model for echocardiography, pretrained on 18M cardiac ultrasound videos to learn artifact-robust anatomical representations.

A native 3D vision transformer self-supervised on unlabeled fluorescence microscopy volumes that generalizes to unseen object classes without retraining or voxel annotations.

A generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.

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.

An fMRI foundation model that trains Vision Transformers on cortical flat-map videos via masked autoencoding, exhibiting power-law scaling on brain activity reconstruction.

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

A self-supervised foundation model for structural brain MRI combining DenseNet and Vision Transformer with Mixture of Experts for multi-task brain disease diagnosis and brain age prediction.