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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 73–96 of 341 filtered models
Vision-language foundation model for kidney cancer CT, covering zero-shot malignancy diagnosis, report generation, and recurrence risk prediction.
Multi-organ segmentation network jointly trained on 10 CT and OCT datasets, reaching 0.83 mean Dice on the RETOUCH retinal fluid challenge.
Dermatology foundation model pretrained on 432,776 skin images, covering malignancy classification, severity grading, and lesion segmentation.
Medical image grounding model that localizes text phrases in CT, MRI, X-ray, ultrasound, endoscopy, dermoscopy, and fundus images.
Mammography report generation model, a LoRA adaptation of MedGemma-4B-it that writes narrative radiology reports carrying BI-RADS assessments.
Red blood cell morphology foundation model pretrained on 1.25 million single-cell crops, released as small, base, and large ViT feature extractors.
Retinal OCT vision-language model that writes layer-by-layer clinical summaries and assigns six-class disease labels from a single B-scan.
Multi-sequence MRI foundation model pretrained on 336,476 volumetric scans, ranking first on 41 of 44 downstream clinical benchmarks.
Brain MRI model for noninvasive IDH genotyping of glioma, adapting a pretrained SWIN-UNETR backbone to reach 90.6% AUC on an external cohort.
Chest CT masked autoencoder pretrained on over 5,000 volumes, fine-tuned to classify interstitial lung disease under Fleischner Society criteria.
Multi-organ CT registration model that aligns thorax, abdomen, and pelvis in one pass and transfers to unseen datasets without fine-tuning.
Sparse autoencoder for blood-cell microscopy that decomposes hematology foundation model embeddings into expert-validated sub-cellular concepts.
Open medical multimodal models from Google, built on Gemma 3 with a medically tuned SigLIP vision encoder for clinical text and image understanding.
Medically tuned SigLIP encoder from Google that maps medical images and text into one embedding space for zero-shot classification and retrieval.
Conditional diffusion model for 7T brain MRI denoising that turns a single 5-minute gradient-echo scan into a four-repetition-quality image.
Automated cryo-EM structure determination that fuses density maps with AlphaFold3 predictions, averaging a TM-score of 0.93 on high-resolution maps.
Single-cell perturbation-response model that predicts transcriptomic and imaging outcomes of unseen genetic perturbations via a VAE with attention.
Family of CNN foundation models pretrained on multimodal radiology images, a domain-specific alternative to ImageNet transfer learning weights.
Biomedical vision-language assistant for medical visual question answering, pairing Phi-2 with a vision encoder in a 4.2B-parameter model.
Self-supervised medical imaging foundation model pretrained on 3.3 million CT, X-ray, ultrasound, pathology, OCT, fundus, and dermoscopy images.
Histopathology image synthesis from a latent diffusion model conditioned jointly on unpaired diagnostic text reports and cell-type masks.
Text-prompted pathology image segmentation across 160 tissue, cell, and nuclei categories, replacing point and box inputs with natural language.
5.6M-parameter multimodal foundation model fusing fMRI time series with diffusion-MRI structural connectivity in a shared ROI embedding space.