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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 169–192 of 249 filtered models
Pathology image embeddings supervised by spatial transcriptomics instead of text captions, aligned over 697K image-gene expression pairs.
Slide-level pathology foundation model turning whole-slide images into reusable embeddings for classification, retrieval, and report generation.
Histopathology image synthesis steered by a hand-drawn coarse tissue mask, refined into a fine-grained semantic mask before H&E or IHC rendering.
Renal pathology segmentation model resolving 14 glomerular tissue, cell, and lesion classes in human and mouse whole-slide images.
Spatial transcriptomics foundation model aligning histology with gene expression at spot and neighborhood scale for zero-shot tissue domain calling.
Multi-scale latent diffusion model for histopathology that synthesizes tissue patches at any magnification and composes them into 4096-pixel images.
Virtual staining diffusion model that converts kidney histology between H&E, Masson's trichrome, PAS, and PASM in any direction from one checkpoint.
Prostate histopathology classifiers that split H&E tissue into benign, Gleason 3, 4 and 5 patches and aggregate those calls into an ISUP grade group.
Lung cancer histopathology model that predicts spread through air spaces from whole-slide images using a feature-interactive Siamese graph encoder.
Histopathology foundation model aligned to spatial transcriptomics by a cross-modal ranking loss, embedding H&E patches without gene input.
Slide-level histopathology encoder that contrastively aligns tile embeddings from several patch foundation models through a Mamba-2 aggregator.
Histopathology model predicting extrachromosomal DNA status from routine H&E slides by first inferring the tumor transcriptome from tile features.
Tissue-specific histological aging clocks that read biological age and per-organ age gaps from H&E whole-slide images and from blood gene expression.
Virtual multiplex immunofluorescence staining from H&E histopathology, imputing the expression and spatial localization of 50 protein biomarkers.
Diffusion autoencoder framework that morphs H&E tiles until a pathology classifier flips, exposing the morphology behind the prediction.
Pan-cancer digital pathology encoder: a ViT-L pretrained with DINOv2 on 400 million H&E patches drawn from 45,000 whole-slide images.
Breast cancer recurrence risk read from an H&E slide and six routine clinical variables, using a frozen pan-cancer pathology encoder.
Region-aware bilingual medical multimodal LLM that handles image- and region-level vision-language tasks across eight imaging modalities.
Spatial transcriptomics prediction from H&E slides, inferring spot-level expression and tumor microenvironment composition in breast cancer.
Vision-language assistant that reads a whole gigapixel pathology slide, answering diagnostic questions and writing slide-level descriptions.
Medical imaging embedding model spanning X-ray, CT, MRI, dermoscopy, OCT, fundus, ultrasound, histopathology and mammography in one encoder.
Pathology report generation from multi-scale whole-slide images, conditioning a frozen PubMed-pretrained GPT-2 on organ-keyed tag-attended features.
Histopathology tile encoder trained by supervised multi-task learning over 16 annotated tasks, matching self-supervised encoders on 6% of patches.