All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 49–70 of 70 filtered models
SAM-Med2D
1.1K258—Medical imaging adaptation of the Segment Anything Model, fine-tuned on 4.6M images and 19.7M masks for promptable segmentation across 10 modalities.
Imaging82OpennessRadFM
561263—Radiology foundation model that reads interleaved 2D and 3D scans with text for diagnosis, visual question answering, and report generation.
ImagingLanguage model84OpennessMed-Flamingo
452618—Multimodal medical vision-language model for few-shot visual question answering, learning new imaging tasks from in-context examples at inference.
PathologyLanguage model18OpennessMed-PaLM M
—552—Google's generalist multimodal biomedical AI that encodes clinical text, medical images, and genomics with a single set of weights across 14 tasks.
ImagingLanguage model25OpennessMedLSAM
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.
Imaging28OpennessLLaVA-Med
2.2K1.9K12.3KBiomedical vision-language assistant for question answering on radiology and pathology images, adapted from LLaVA on PubMed Central captions.
PathologyLanguage model28OpennessMedBLIP
5789—Vision-language framework for 3D medical image diagnosis and visual question answering, bridging frozen image encoders and LLMs, shown on brain MRI.
ImagingLanguage model35OpennessMedVInT
236367—Generative medical visual question answering model that pairs a vision encoder with a language model, trained on the 227k-pair PMC-VQA dataset.
PathologyLanguage model83OpennessSTU-Net
372159—Scalable and transferable U-Net family (14M–1.4B parameters) for 3D medical image segmentation, supervised-pretrained on TotalSegmentator.
Imaging82OpennessPMC-CLIP
241——Biomedical vision-language model trained contrastively on 1.6M figure-caption pairs mined from PubMed Central open-access articles.
PathologyImaging63OpennessSelf-supervised pretraining for 3D medical images that learns anatomical correspondences between scans, giving encoders transferable to segmentation.
Imaging17OpennessBiomedCLIP
128665878.5KBiomedical vision-language model trained contrastively on 15M PubMed Central figure-caption pairs for zero-shot classification, retrieval, and VQA.
Imaging61OpennessPTUnifier
7853—Chinese University of Hong Kong, Shenzhen +2 othersFebruary 17, 2023chest_x_rayfoundation_modelimage_text_retrieval+8Medical vision-language pretraining unifying fusion-encoder and dual-encoder designs, handling image-only, text-only, and paired inputs in one model.
PathologyLanguage model56OpennessPCRLv2
10083—Self-supervised pretraining framework for medical imaging that unifies pixel restoration with contrastive learning across 2D and 3D image backbones.
Imaging71OpennessRoentGen
88146—Text-conditioned latent diffusion model that generates synthetic chest X-rays from free-form radiology prompts by adapting Stable Diffusion.
ImagingLanguage model20OpennessCheXzero
234527—Self-supervised vision-language model for zero-shot detection of chest X-ray pathologies, trained on image-report pairs without explicit labels.
ImagingPathology70OpennessM3AE
134192—Shenzhen Research Institute of Big Data +2 othersSeptember 15, 2022autoencoderimage_text_retrievalmultimodal+5Self-supervised medical vision-and-language pretraining via multi-modal masked autoencoders that reconstruct masked image patches and text tokens.
PathologyLanguage model29Openness- Shenzhen Research Institute of Big Data +2 othersSeptember 15, 2022chest_x_rayfoundation_modelimage_text_retrieval+7
Medical vision-language pretraining framework that injects structured medical knowledge into radiology image-text learning for VQA and retrieval.
ImagingLanguage model29Openness PubMedCLIP
1833116.7KMedical-domain CLIP fine-tuned on radiology image-caption pairs from ROCO, serving as a drop-in visual encoder for medical visual question answering.
PathologyLanguage model75OpennessMed3D
2.2K681—Pretrained 3D-ResNet backbones for volumetric medical image analysis, co-trained across eight CT and MRI segmentation datasets for transfer learning.
Imaging75Openness