All Competitors

Predicts 16-channel multiplex immunofluorescence from H&E histology using a ViT foundation-model encoder, validated on five external datasets.

Latent diffusion model for H&E-to-IHC stain transfer, dual-conditioned on pathology foundation-model embeddings, covering HER2, Ki67, ER, and PR.

Pathology video-language model that reads histopathology clips and produces a step-by-step histological description plus a sign-out diagnosis.

Vision transformer that regresses Ki-67-positive and -negative nuclei counts in breast histopathology and scores the index from H&E slides alone.

Multi-resolution vision-language foundation model for histopathology, pretrained on 34M TCGA image-text pairs across four magnifications.

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

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.