All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–14 of 14 filtered models
BrainDINO
43—Emory University +2 othersApril 30, 2026brain_age_estimationdisease_classificationfoundation_model+6Self-supervised brain MRI foundation model built on DINOv3, pretrained on roughly 6.6 million unlabeled axial slices for neuroimaging tasks.
Imaging49OpennessNeuroVFM
50—465University of Michigan +1 otherNovember 23, 2025ctfoundation_modeljoint_embedding_predictive_architecture+8Generalist neuroimaging vision foundation model pretrained on 5.24M clinical MRI and CT volumes for radiologic diagnosis and report generation.
Imaging57OpennessNeuroRAD-FM
—4—Neuro-oncology foundation model for brain tumor MRI, using distributionally robust pretraining for molecular subtyping and survival prediction.
Imaging23OpennessSwin-BOB
5016—3D MRI organ segmentation foundation model built on Swin-UNETR and trained on the UKBOB whole-body dataset covering 72 organs and skeletal structures.
Imaging64OpennessBME-X
6871—Tissue-aware foundation model that restores brain MRI quality across motion correction, super-resolution, denoising, and harmonization.
Imaging70OpennessTUMSyn
45——Text-guided MRI synthesis model that generates brain MR sequences and resolutions on demand from routine scans using imaging-metadata prompts.
Imaging28OpennessBrainSegFounder
1576—3D vision-transformer foundation model for multimodal neuroimage segmentation, pretrained self-supervised on brain MRI from 41,400 participants.
Imaging51OpennessM3D
45138861Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.
ImagingLanguage model77OpennessuniGradICON
2274—Foundation model for medical image registration that aligns CT and MRI across anatomies and modalities without per-pair optimization or retraining.
Imaging65OpennessLVM-Med
217——Self-supervised vision foundation model pretrained on 1.3M medical images via second-order graph matching, for segmentation and classification.
Imaging28OpennessSelf-supervised pretraining for 3D medical images that learns anatomical correspondences between scans, giving encoders transferable to segmentation.
Imaging17OpennessModels Genesis
787——Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.
Imaging20OpennessMed3D
2.2K28—Pretrained 3D-ResNet backbones for volumetric medical image analysis, co-trained across eight CT and MRI segmentation datasets for transfer learning.
Imaging75Openness