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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 97–120 of 249 filtered models
Contrastive alignment framework that projects H&E histology and single-cell transcriptomic foundation model embeddings into one shared latent space.
Sparse autoencoder for blood-cell microscopy that decomposes hematology foundation model embeddings into expert-validated sub-cellular concepts.
Attention-based multiple instance learning heads for whole-slide pathology, pretrained on a 108-way pan-cancer slide classification task.
Histopathology foundation model trained with direct slide-level supervision on 37k whole-slide images. Averages 0.784 AUROC on 10 biomarker tasks.
Medically tuned SigLIP encoder from Google that maps medical images and text into one embedding space for zero-shot classification and retrieval.
Whole-slide histopathology foundation model trained end-to-end on slide-level labels across 18 tasks, on 5% of the energy of SSL-trained peers.
Self-supervised medical imaging foundation model pretrained on 3.3 million CT, X-ray, ultrasound, pathology, OCT, fundus, and dermoscopy images.
Histopathology image synthesis from a latent diffusion model conditioned jointly on unpaired diagnostic text reports and cell-type masks.
Text-prompted pathology image segmentation across 160 tissue, cell, and nuclei categories, replacing point and box inputs with natural language.
Genome-anchored histopathology embeddings that predict molecular biomarkers, subtypes, and survival from whole-slide images alone at inference.
Medical multimodal LLM (2B and 8B) trained for generalizable, step-by-step clinical reasoning via Mentor-Intern Collaborative Search.
Generative histopathology foundation model: a diffusion transformer trained on 30M H&E tiles, conditioned on self-supervised slide embeddings.
Spatial proteomics foundation model, marker-aware and panel-agnostic, pretrained on 47 million multiplexed tissue-imaging patches from 175 markers.
Agent-based pathology model that navigates whole-slide images by zooming and panning like a pathologist, scoring 88.6% on the PathMMU-HR2 benchmark.
Cytogenetics foundation model detecting numerical and structural chromosome abnormalities from metaphase images, pretrained on 84,000 specimens.
Tri-modal pathology foundation model aligning whole-slide images, transcriptomes, and diagnostic reports, and running on any subset of the three.
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.