All Competitors

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

Showing 18 of 8 filtered models

  • Uni-Hema

    1
    Information Technology University of the Punjab +1 otherNovember 18, 2025classificationcnnfoundation_model+8

    Digital hematopathology foundation model unifying blood-cell detection, classification, segmentation, and visual question answering.

    Pathology
    8Openness
  • MetaboFM

    2
    Georgia Institute of TechnologyOctober 23, 2025classificationfoundation_modelmass_spectrometry_imaging+6

    Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.

    MetabolomicsSpatial omicsImaging
    10Openness
  • Monash UniversityJune 23, 2025classificationconnectomicsfmri+7

    5.6M-parameter multimodal foundation model fusing fMRI time series with diffusion-MRI structural connectivity in a shared ROI embedding space.

    ImagingBiosignals
    71Openness
  • FetalCLIP

    7022
    Mohamed bin Zayed University of Artificial Intelligence +1 otherFebruary 20, 2025classificationcontrastive_learningfoundation_model+7

    Vision-language foundation model for fetal ultrasound, pretrained on 210,035 image-text pairs for plane classification, biometry, and segmentation.

    Imaging
    13Openness
  • USFM

    350112
    Fudan UniversityAugust 1, 2024classificationfoundation_modelimage_restoration+4

    Ultrasound foundation model pretrained on over two million multi-organ images, transferring to segmentation, classification, and image enhancement.

    Imaging
    22Openness
  • LVM-Med

    217100
    University of Stuttgart +6 othersJune 20, 2023classificationcontrastive_learningct+11

    Self-supervised vision foundation model pretrained on 1.3M medical images via second-order graph matching, for segmentation and classification.

    Imaging
    28Openness
  • PCRLv2

    10083
    The University of Hong Kong +1 otherJanuary 2, 2023chest_x_rayclassificationcnn+8

    Self-supervised pretraining framework for medical imaging that unifies pixel restoration with contrastive learning across 2D and 3D image backbones.

    Imaging
    71Openness
  • Arizona State University +1 otherAugust 19, 2019classificationcnnct+7

    Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.

    Imaging
    20Openness