All Competitors

Every biological foundation model, evaluated and ranked by the bio.rodeo team

Showing 145158 of 158 filtered models

  • UCE

    312174
    Stanford UniversityNovember 29, 2023cross_speciesfoundation_modeltransformer+1

    Single-cell foundation model producing species-agnostic cell embeddings by representing genes through frozen ESM-2 protein language model embeddings.

    Single-cell
    65Openness
  • CellSAM

    20843
    Van Valen LabNovember 20, 2023cell_biologyfoundation_modelsegmentation+2

    Universal cell segmentation model adapting Meta's SAM to segment mammalian cells, yeast, and bacteria across imaging modalities without retraining.

    Imaging
    32Openness
  • GPN

    34931.7K
    Song LabOctober 31, 2023dnafoundation_modelgenomics+2

    DNA language model for genome-wide variant effect prediction, trained by masked language modeling on multispecies genomes with no labeled data.

    DNA & Gene
    93Openness
  • SAM-Med3D

    944182
    Shanghai AI LaboratoryOctober 23, 2023foundation_modelradiologysegmentation+3

    Fully 3D promptable segmentation foundation model for volumetric CT and MR, encoding whole volumes so anatomy can be segmented from one prompt point.

    Imaging
    96Openness
  • City University of Hong Kong +2 othersOctober 1, 2023cnnct_imagingmultimodal+6

    Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.

    Imaging
    26Openness
  • UniBrain

    39183
    Shanghai Jiao Tong University +3 othersSeptember 13, 2023brain_mricnncontrastive_learning+6

    Vision-language pre-training framework for universal brain MRI diagnosis, learning from imaging-report pairs to cover more than ten brain diseases.

    Imaging
    35Openness
  • BrainLM

    18130
    Yale University +2 othersSeptember 12, 2023brain_state_forecastingclinical_variable_predictionfmri+7

    fMRI foundation model pretrained with masked autoencoding on roughly 6,700 hours of recordings for clinical prediction and network discovery.

    Biosignals
    25Openness
  • SAM-Med2D

    1.1K258
    Shanghai AI LaboratoryAugust 30, 2023foundation_modelhistologyradiology+4

    Medical imaging adaptation of the Segment Anything Model, fine-tuned on 4.6M images and 19.7M masks for promptable segmentation across 10 modalities.

    Imaging
    82Openness
  • PLIP

    38283145.1K
    Stanford UniversityAugust 28, 2023contrastive_learningfoundation_modelhistology+3

    Vision-language foundation model for pathology, fine-tuned from CLIP on 208,414 image-text pairs for zero-shot classification and image retrieval.

    Imaging
    25Openness
  • Med-PaLM M

    552
    Google Research +1 otherJuly 26, 2023generativegenomicshistology+9

    Google's generalist multimodal biomedical AI that encodes clinical text, medical images, and genomics with a single set of weights across 14 tasks.

    ImagingLanguage model
    25Openness
  • TULIP

    1343
    Ecole Normale SuperieureJuly 19, 2023antibodydrug_discoverylanguage_model+3

    Unsupervised transformer language model for TCR-epitope binding prediction that generalizes to unseen epitopes without needing negative examples.

    Protein
    60Openness
  • MedBLIP

    5789
    Shanghai Jiao Tong UniversityMay 18, 2023brain_mricomputer_aided_diagnosisimage_classification+7

    Vision-language framework for 3D medical image diagnosis and visual question answering, bridging frozen image encoders and LLMs, shown on brain MRI.

    ImagingLanguage model
    35Openness
  • BiomedCLIP

    128665878.5K
    Microsoft ResearchMarch 1, 2023contrastive_learningfoundation_modelimage_analysis+4

    Biomedical vision-language model trained contrastively on 15M PubMed Central figure-caption pairs for zero-shot classification, retrieval, and VQA.

    Imaging
    61Openness
  • CheXzero

    234527
    Stanford UniversitySeptember 15, 2022chest_radiographycontrastive_learningimage_classification+7

    Self-supervised vision-language model for zero-shot detection of chest X-ray pathologies, trained on image-report pairs without explicit labels.

    ImagingPathology
    70Openness