Loading…
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Histopathology tile encoder trained by supervised multi-task learning over 16 annotated tasks, matching self-supervised encoders on 6% of patches.
Weakly supervised histopathology foundation model pretrained on 60,530 whole-slide images for cancer detection, prognosis, and molecular prediction.
Open-source, lightweight generalist vision-language foundation model for diverse biomedical imaging and text tasks.
Medical vision-language model trained on the MedTrinity-25M dataset, answering questions and generating text about radiology and histology images.
Slide-level pathology foundation model that learns whole-slide embeddings by aligning multiple stains of the same tissue during pretraining.
Histopathology image translation with diffusion, moving H&E tiles between stains, tumor types, and organ sites and editing them from omics profiles.
Histopathology vision transformer with 1.1B parameters, pretrained on patches from 500,000 H&E whole-slide images across 4,000 clinical practices.
Multimodal vision-language copilot for pathology that answers open-ended questions about histology images and reasons about differential diagnoses.
Histopathology multimodal assistant answering questions about H&E patches, pairing a pathology-trained CLIP tower with a 13B Vicuna language model.
Histopathology vision-language model classifying H&E patches zero-shot from text prompts, trained on 1.6M captions written for whole-slide crops.
Open medical multimodal LLMs (7B and 34B) for visual question answering over radiology, pathology, and endoscopy images, trained on PubMedVision.
DeepLabV3 segmentation model that separates tissue from glass background in H&E and IHC whole-slide images, as used by the HEST-Library.
Whole-slide histopathology foundation model pretrained on 1.3 billion image tiles from 171,189 clinical slides spanning 31 tissue types.
Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.
Lightweight mixture-of-experts medical vision-language model routing visual question answering and image classification to domain-specific experts.
Histopathology vision-language foundation model pretrained on 1.17 million image-caption pairs with contrastive and captioning objectives.
Computational pathology foundation model (ViT-L/16, DINOv2) pretrained on over 100 million H&E tiles from more than 100,000 whole-slide images.
Fine-grained cell-type abundance prediction from H&E histology, transferring to unseen cohorts and large slide archives without any retraining.
Virtual staining model that generates 11-marker spatially resolved protein multiplexes from routine H&E histopathology whole-slide images.
Self-supervised pathology foundation model with a 300M-parameter vision transformer tile encoder, trained on a multi-stain whole-slide image corpus.