All Competitors

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

Showing 18 of 8 filtered models

  • PlantGeneAnn

    12126
    Huazhong Agricultural UniversityJune 25, 2026dnafoundation_modelgene_structure_annotation+7

    Plant genome foundation model for ab initio gene structure annotation, predicting genes, coding sequences, and exons at single-nucleotide resolution.

    DNA & Gene
    66Openness
  • BrainFM

    208
    Johns Hopkins University +4 othersAugust 30, 2025foundation_modelimage_synthesismulti_task+4

    Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

    Imaging
    75Openness
  • MINIM

    158138
    Peking University +2 othersFebruary 1, 2025data_augmentationdiffusionfoundation_model+9

    Text-to-image diffusion model that generates synthetic medical images across imaging modalities and organs to augment scarce clinical training data.

    Imaging
    41Openness
  • German Cancer Research Center (DKFZ) +5 othersOctober 30, 2024brain_mricnnfoundation_model+7

    Masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on roughly 39,000 brain MRIs for volumetric image segmentation.

    Imaging
    45Openness
  • MoME

    3125
    Beijing Institute of Technology +3 othersMay 16, 2024brain_mricnncurriculum_learning+7

    Universal brain lesion segmentation for multi-modal brain MRI, using a Mixture of Modality Experts to span diverse modalities and lesion types.

    Imaging
    79Openness
  • STU-Net

    372159
    Shanghai AI LaboratoryApril 13, 2023cnnctfoundation_model+5

    Scalable and transferable U-Net family (14M–1.4B parameters) for 3D medical image segmentation, supervised-pretrained on TotalSegmentator.

    Imaging
    82Openness
  • 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