All Competitors

Digital cytology foundation model, a ViT-Base pretrained with iBOT self-supervision on 1.4 million patches from breast, cervical, and thyroid slides.

Whole-slide multimodal LLM for histopathology, pairing a frozen pathology encoder with a LoRA-tuned LLaMA2-7B for pan-cancer diagnostic Q&A.

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

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

Histopathology foundation models trained on up to 100x fewer whole-slide images than peers, matching UNI-2 across 13 tile- and slide-level tasks.

Histopathology foundation model pretrained on over 1 million H&E slides from 800,000 patients. Leads the HEST spatial gene expression benchmark.

Histology nuclei segmentation that adapts SAM to train on several datasets at once, aligning auxiliary domains without diluting the primary one.

Multimodal medical imaging foundation model built for chromosome karyotype analysis, with 92.75% sensitivity for structural abnormality detection.

Histopathology foundation model pretrained with DINOv2 on tiles chosen by unsupervised hierarchical clustering over 350 million whole-slide tiles.

Digital pathology model inferring collagen structure from H&E whole-slide images, trained against polarization imaging of Picrosirius Red slides.

Pathology image restoration recovering all-in-focus histology from single defocused focal planes, guided by semantic, defocus, and edge prompts.

Learned compression autoencoders for histopathology whole-slide images, tuned so reconstructions preserve the features downstream models rely on.

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

Clinical imaging encoder multitask-pretrained across X-ray, mammography, dermoscopy, fundus, ultrasound, CT, and histopathology for few-shot transfer.

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

Histopathology foundation model pretrained on 300K whole-slide images across 20 tissue types and validated on 112 clinical-grade downstream tasks.

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

Vision-language model that drafts case-level pathology reports for cutaneous melanocytic lesions and retrieves slides and reports across modalities.

Histology-anchored framework pairing an H&E foundation model with a cellular hypergraph to predict single-cell multi-omics from tissue images.

Histopathology foundation model for whole-slide cancer diagnosis, with a ViT-L encoder pretrained by DINOv2 on a million-slide hospital archive.

Whole-slide pathology embedding framework that ranks tiles, keeps only the 25 most informative, and encodes a slide in 2.27 seconds.

Medical vision-language model that unifies image comprehension and generation in one autoregressive transformer via heterogeneous LoRA adapters.

Self-supervised CT foundation model that segments pathology as visual anomalies, pretrained on 30,000 unlabeled 3D volumes with no annotation.