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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 73–96 of 249 filtered models
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.
Nuclei detection and classification in histopathology images, using one specialised decoder per cell type over a shared convolutional encoder.
Clinical laboratory morphology vision-language model that identifies cells, describes them and localizes them in blood, urine and stool microscopy.
Self-supervised foundation model for human cortical cytoarchitecture, encoding histological brain sections into anatomically meaningful features.
Frozen-section pathology foundation model for intraoperative diagnosis, LoRA-adapted from Virchow2 and validated in a prospective surgical study.
Spatial gene expression prediction from H&E histology that decodes raw integer counts coarse to fine instead of regressing log-transformed values.
Pan-tissue quality-control model that predicts RNA integrity and autolysis from H&E whole-slide images using frozen UNI foundation model embeddings.
Chest radiograph and report encoder pretrained contrastively on a million pairs, whose frozen embeddings train classifiers from a few hundred cases.
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.
Slide-level pathology foundation model fusing five frozen tile encoders into one whole-slide embedding for biomarker and therapy response prediction.
Dermatology foundation model pretrained on 432,776 skin images, covering malignancy classification, severity grading, and lesion segmentation.
Histology vision transformer with 80M parameters that predicts spatial gene expression from H&E tissue images and transfers to tumor detection.
Red blood cell morphology foundation model pretrained on 1.25 million single-cell crops, released as small, base, and large ViT feature extractors.
Histopathology segmentation model aligning SAM to clinical intent through direct preference optimization, tested zero-shot on 12 external datasets.
Histopathology and multi-omics foundation model pretrained with masked omics modeling on 4,718 pan-cancer TCGA cases spanning 32 cancer types.