All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–17 of 17 filtered models
Merlin
4531366.9K3D vision-language foundation model for abdominal CT, pretrained on scans, radiology reports, and EHR codes for zero-shot interpretation.
ImagingLanguage model54OpennessNeuroVFM
554654University 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.
Imaging57OpennessM3FM
1938—Multimodal medical imaging foundation model for zero-shot clinical diagnosis and report generation from chest X-ray and CT in English and Chinese.
ImagingLanguage model60OpennessM3D
454172924Multimodal large language model for 3D medical imaging that handles report generation, visual question answering, and segmentation on CT volumes.
ImagingLanguage model77OpennessuniGradICON
22871—Foundation model for medical image registration that aligns CT and MRI across anatomies and modalities without per-pair optimization or retraining.
Imaging65OpennessVoCo
230113—Hong Kong University of Science and TechnologyFebruary 27, 2024contrastive_learningctfoundation_model+5Self-supervised pretraining framework for 3D medical image encoders that learns anatomy by predicting where a sub-volume sits within a CT scan.
Imaging69OpennessT3D
—16—Vision-language pretraining for 3D CT volumes, aligning scans with their radiology reports for zero-shot classification, retrieval, and segmentation.
ImagingLanguage model12OpennessSegVol
386122747Promptable 3D foundation model for volumetric CT segmentation, covering over 200 anatomical categories through point, box, and free-text prompts.
Imaging100OpennessMIS-FM
25050—University of Electronic Science and Technology of China +3 othersJune 29, 2023cnnctfoundation_model+3Self-supervised foundation model for 3D medical image segmentation, pretrained on roughly 110,000 unannotated CT volumes via Volume Fusion.
Imaging73OpennessMedLSAM
52282—3D CT localization foundation model that pairs MedLAM with SAM to segment any anatomical structure at a fixed, dataset-independent annotation cost.
Imaging76OpennessLVM-Med
217100—Self-supervised vision foundation model pretrained on 1.3M medical images via second-order graph matching, for segmentation and classification.
Imaging28OpennessSTU-Net
372159—Scalable and transferable U-Net family (14M–1.4B parameters) for 3D medical image segmentation, supervised-pretrained on TotalSegmentator.
Imaging82OpennessSelf-supervised pretraining for 3D medical images that learns anatomical correspondences between scans, giving encoders transferable to segmentation.
Imaging17OpennessPCRLv2
10083—Self-supervised pretraining framework for medical imaging that unifies pixel restoration with contrastive learning across 2D and 3D image backbones.
Imaging71OpennessModels Genesis
788407—Self-supervised 3D pretrained models for CT and MRI that learn anatomical representations from unlabeled volumes and transfer to segmentation tasks.
Imaging20OpennessMed3D
2.2K681—Pretrained 3D-ResNet backbones for volumetric medical image analysis, co-trained across eight CT and MRI segmentation datasets for transfer learning.
Imaging75Openness