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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–72 of 224 filtered models
Renal pathology foundation model whose learning unit is a whole detected glomerulus, pretrained on over a million of them across four biopsy stains.
Renal pathology foundation model self-supervised on a million kidney biopsy tiles spanning glomeruli, interstitium, and surrounding structures.
Vision-omics finetuning that aligns pathology foundation models with spatial transcriptomics so morphology features predict local gene expression.
Cross-species multimodal foundation model of immunology and inflammation, harmonizing transcriptomics and histology into patient-level embeddings.
Pan-cancer model predicting spatial gene expression from H&E histology using conditional flow matching with a mixture-of-experts velocity field.
Generative foundation model that imputes genes and denoises spatial transcriptomics, conditioned on H&E histology, scRNA-seq, and spatial priors.
Pathology foundation model that aligns whole-slide images with genomic, epigenetic, and transcriptomic data for patient-level tumor representations.
Tissue imaging foundation model pretrained on matched H&E histology and spatial proteomics for cross-modal inference and zero-shot retrieval.
Multi-modal contrastive model that aligns H&E histopathology with spatial transcriptomics across tissue scales to predict gene expression from images.
Histopathology model that predicts single-cell type composition and reconstructs spatial gene expression from H&E slides, with no molecular assay.
Digital hematopathology foundation model unifying blood-cell detection, classification, segmentation, and visual question answering.
Histopathology model predicting TP53 mutation status, TP53 RNA expression, and tumour taxonomy from H&E whole-slide images across 32 solid cancers.
Pathology foundation model that fuses global patch and cell-level tokens via joint-weighted attention pooling for H&E-based biomarker detection.
Histopathology foundation model extracting general-purpose features from H&E patches by distilling the UNI, Phikon, and CONCH pathology encoders.
Self-supervised foundation model for human cortical cytoarchitecture, encoding histological brain sections into anatomically meaningful features.
Pan-tissue quality-control model that predicts RNA integrity and autolysis from H&E whole-slide images using frozen UNI foundation model embeddings.
Histopathology model predicting homologous recombination deficiency from H&E slides in ovarian cancer, reaching 0.846 AUC and 0.938 specificity.
Predicts virtual single-cell spatial transcriptomics from H&E histology using frozen pathology foundation models and spot-level supervision.
Centrosome segmentation framework chaining YOLOv11 detection, U-Net refinement, and StarDist cell boundaries across immunofluorescence and IHC tissue.
Image-to-image translation from label-free phase-contrast microscopy to H&E-like images, so pretrained histopathology models run on live cells.
Histopathology classifier separating atypical from normal mitotic figures, LoRA-adapting a DINOv3 vision transformer with 1.3M trainable parameters.
Dermatology foundation model pretrained on 432,776 skin images, covering malignancy classification, severity grading, and lesion segmentation.