All Competitors

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

Showing 73–78 of 78 filtered models

  • MedVInT

    236367—
    Shanghai Jiao Tong University +1 otherMay 17, 2023generativehistologymedical_image_understanding+6

    Generative medical visual question answering model that pairs a vision encoder with a language model, trained on the 227k-pair PMC-VQA dataset.

    PathologyLanguage model
    83Openness

  • PMC-CLIP

    241——
    Shanghai Jiao Tong UniversityMarch 13, 2023cnncontrastive_learninghistology+7

    Biomedical vision-language model trained contrastively on 1.6M figure-caption pairs mined from PubMed Central open-access articles.

    PathologyImaging
    63Openness

  • PTUnifier

    7853—
    Chinese University of Hong Kong, Shenzhen +2 othersFebruary 17, 2023chest_x_rayfoundation_modelimage_text_retrieval+8

    Medical vision-language pretraining unifying fusion-encoder and dual-encoder designs, handling image-only, text-only, and paired inputs in one model.

    PathologyLanguage model
    56Openness

  • M3AE

    134192—
    Shenzhen Research Institute of Big Data +2 othersSeptember 15, 2022autoencoderimage_text_retrievalmultimodal+5

    Self-supervised medical vision-and-language pretraining via multi-modal masked autoencoders that reconstruct masked image patches and text tokens.

    PathologyLanguage model
    29Openness

  • 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

  • PubMedCLIP

    1833116.7K
    Hasso Plattner InstituteDecember 27, 2021cnncontrastive_learninghistology+8

    Medical-domain CLIP fine-tuned on radiology image-caption pairs from ROCO, serving as a drop-in visual encoder for medical visual question answering.

    PathologyLanguage model
    75Openness