All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–7 of 7 filtered models
BrainFM
176—A modality-agnostic, multi-task foundation model for human brain imaging that runs five core tasks across uncalibrated CT and MRI without retraining.
Imaging75OpennessMINIM
158127—A self-improving text-to-image diffusion foundation model that generates synthetic medical images across multiple modalities and organs to augment downstream clinical AI tasks.
Imaging41OpennessSpark3D (S3D)
15434—A masked-autoencoder foundation model that pre-trains a 3D Residual Encoder U-Net on ~39k brain MRIs to improve volumetric medical image segmentation.
Imaging45OpennessMoME
3124—A universal foundation model for brain lesion segmentation on multi-modal brain MRI, using a Mixture of Modality Experts to handle diverse modalities and lesion types.
Imaging79OpennessSTU-Net
370152—Scalable and transferable U-Net family (14M–1.4B parameters) for 3D medical image segmentation, supervised-pretrained on TotalSegmentator.
Imaging82OpennessPCRLv2
10082—Self-supervised pre-training framework for medical image analysis that unifies pixel restoration with contrastive feature comparison across 2D and 3D modalities.
Imaging71OpennessModels Genesis
782398—Self-supervised 3D pretrained models that learn anatomical representations from unlabeled medical scans for transfer learning to segmentation and classification tasks.
Imaging20Openness