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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 145–168 of 386 filtered models
Retinal fundus foundation model conditioned on patient age and sex, pretrained on 1.0 million colour photographs from 292,000 patients.
Surgical video foundation model pretrained by entropy-maximizing compression on 0.78M unlabeled frames from 2,122 minimally invasive procedures.
Predicts 16-channel multiplex immunofluorescence from H&E histology using a ViT foundation-model encoder, validated on five external datasets.
Latent diffusion model for H&E-to-IHC stain transfer, dual-conditioned on pathology foundation-model embeddings, covering HER2, Ki67, ER, and PR.
Zero-shot tumor segmentation on CT and MRI that reads text-prompted anomaly attention maps out of a frozen medical foundation diffusion model.
Sparse-view CBCT reconstruction foundation model pretrained on 8,407 CT volumes, recovering full 3D anatomy from as few as six X-ray projections.
Generalist cell segmentation model pairing SAM's ViT-L encoder with Cellpose flow fields, outperforming average human annotators on its benchmark.
Brain MRI segmentation foundation model trained on 66,000+ image-label pairs across 14 MRI sub-modalities, with a hypergraph dynamic adapter.
Universal foundation model that jointly generates diagnostic text and segments the corresponding targets across ten biomedical imaging modalities.
Multi-resolution vision-language foundation model for histopathology, pretrained on 34M TCGA image-text pairs across four magnifications.
Ophthalmic foundation model behind the GlobeReady platform, reading fundus and OCT scans by frozen-encoder retrieval instead of per-site fine-tuning.
Unified electron microscopy image analysis toolkit built on EM-DINO, a vision foundation model pretrained on 5 million diverse EM images.
Promptable medical image segmentation trained only on procedurally generated synthetic images, then applied zero-shot to CT, MRI, and ultrasound.
Complex-valued diffusion model generating synthetic MRI k-space phase from magnitude images, raising k-space skull-stripping Dice from 41.1% to 80.1%.
3D MRI organ segmentation foundation model built on Swin-UNETR and trained on the UKBOB whole-body dataset covering 72 organs and skeletal structures.
RNA 3D structure reconstruction from cryo-EM density maps, using a 3D U-Net that predicts 18 atom types and assigns sequence by global alignment.
Promptable 3D medical image and video segmentation foundation model fine-tuned from SAM 2.1, cutting lesion annotation time by up to 92%.
General medical vision-language model trained with reinforcement learning to reason step by step over medical images for diagnosis and visual QA.
Hypergraph foundation model for brain disease diagnosis from resting-state fMRI, self-supervised on high-order connectivity among brain regions.
Histology nuclei segmentation that adapts SAM to train on several datasets at once, aligning auxiliary domains without diluting the primary one.
Multimodal medical imaging foundation model built for chromosome karyotype analysis, with 92.75% sensitivity for structural abnormality detection.
Whole-heart segmentation foundation model for CT and MRI, pretrained self-supervised on unlabeled cardiac scans with an xLSTM-UNet backbone.