All Competitors

Histopathology foundation model for uterine malignancies that orders whole-slide morphology into continuous, progression-associated tumor states.

Renal tumor histopathology model that detects tissue regions, classifies nine subtypes, grades nuclei and scores prognosis from a single H&E slide.

Self-supervised colorectal histopathology model turning H&E tiles into interpretable phenotype clusters and a disease-free survival risk score.

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

Transcriptome foundation model for precision oncology, generalizing zero-shot across tissue, plasma cfRNA, and tumor-educated platelet modalities.

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

Histopathology model predicting TP53 mutation status, TP53 RNA expression, and tumour taxonomy from H&E whole-slide images across 32 solid cancers.

Histopathology foundation model extracting general-purpose features from H&E patches by distilling the UNI, Phikon, and CONCH pathology encoders.

Neuro-oncology foundation model for brain tumor MRI, using distributionally robust pretraining for molecular subtyping and survival prediction.

Vision-language foundation model for kidney cancer CT, covering zero-shot malignancy diagnosis, report generation, and recurrence risk prediction.

Histopathology and multi-omics foundation model pretrained with masked omics modeling on 4,718 pan-cancer TCGA cases spanning 32 cancer types.

Pathology vision foundation model backbone hybridizing state space models with a ViT. 57% higher leave-one-study-out correlation on gene expression.

Whole-slide histopathology foundation model trained end-to-end on slide-level labels across 18 tasks, on 5% of the energy of SSL-trained peers.

Histopathology foundation model aligning H&E image patches with co-registered spatial transcriptomics through a mixture of data experts.

Genome-anchored histopathology embeddings that predict molecular biomarkers, subtypes, and survival from whole-slide images alone at inference.

Multimodal slide-level pathology foundation model trained by clinical-dialogue supervision on 2.3M whole-slide images and 14M Q&A pairs.

Tri-modal pathology foundation model aligning whole-slide images, transcriptomes, and diagnostic reports, and running on any subset of the three.

Spatial transcriptomics prediction from H&E whole-slide images. One generative checkpoint covers 38,984 genes and 17 organs without fine-tuning.

Histopathology encoder pretrained on synthetic H&E patches mixed 1:1 with real TCGA tiles, outperforming UNI on lung and lymph node subtyping.

Histopathology encoder pretrained entirely on prototype-guided synthetic H&E patches, matching models trained on 60-760x more real patient tiles.

Histopathology foundation model pretrained with BEiT masked image modeling on 11M+ tissue image tiles for cancer diagnosis and survival prediction.

Slide-level pathology foundation model that vector-quantizes tile patch tokens at 64x compression, keeping spatial detail for whole-slide analysis.

Histopathology model predicting gene expression and DNA methylation from H&E slides across 23 cancer types, fusing FFPE and fresh-frozen predictors.