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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 121–144 of 224 filtered models
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.
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.
Cervical cytology screening system pretrained on 127,471 whole-slide images from 48 centers, with test-time adaptation for new clinical sites.
Pathology vision-language model that adds lightweight adaptors and multi-granular prompt learning for few-shot whole-slide image classification.
Nuclei detection and classification in histopathology whole slide images, replacing segmentation masks with direct transformer-based set prediction.
Segment Anything fine-tuned for nucleus segmentation in histopathology, supporting automatic and interactive annotation on unseen tissue images.
Crossmodal diffusion model synthesizing bulk tumor gene expression from H&E whole-slide images, so grading and survival prediction need no RNA assay.
Skin cancer subtype classification from H&E whole slide images, with one vision transformer reading patches at 10x, 20x, 40x and 400x.
Slide-level pathology foundation model that encodes a whole-slide image of any size into one embedding, supervised by paired sequencing data.
Histopathology image translation model that standardizes H&E staining style, then generates virtual collagen, reticulin, and trichrome fiber images.
Biomedical vision-language model aligning image regions to UMLS clinical concepts, for zero-shot diagnosis across 10 imaging modalities.
Computational tumour-infiltrating lymphocyte scoring for breast cancer, regressing the stromal TIL percentage from H&E slide features in one step.
Histopathology foundation model pretrained on 200 million H&E and immunohistochemistry tiles from more than 350,000 whole-slide images.
Vision-language foundation model for precision oncology, pretrained on 50M pathology images and 1B text tokens via unified masked modeling.
Tumor microenvironment segmentation on H&E slides, labeling 13 tissue and cell components from a single model in semantic or panoptic form.
Histopathology vision-language foundation model that folds a disease knowledge graph into pretraining for zero-shot cancer detection and subtyping.
Histopathology vision-language model handling image patches and gigapixel slides in one 15B checkpoint, across classification, VQA, and captioning.
Self-supervised histopathology encoder that models tissue entities as a graph and pretrains by latent graph diffusion over masked subgraphs.