All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 121–141 of 141 filtered models
RETFound
660938105University College London +1 otherSeptember 13, 2023disease_detectionfoundation_modelimage_classification+7Self-supervised foundation model for retinal imaging, pretrained on 1.6 million unlabelled fundus and OCT scans to detect ocular and systemic disease.
ImagingPathology30OpennessSAM-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 model25OpennessMIS-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.
Imaging28OpennessHeartBEiT
25118—Vision transformer for electrocardiograms that reads the printed 12-lead ECG as an image, enabling data-efficient diagnosis from few labeled examples.
Biosignals30OpennessLLaVA-Med
2.2K1.9K12.3KBiomedical vision-language assistant for question answering on radiology and pathology images, adapted from LLaVA on PubMed Central captions.
PathologyLanguage model28OpennessPathAsst
136104—Multimodal pathology assistant that answers questions about histology and cytology images, pairing the PathCLIP vision encoder with a Vicuna-13B LLM.
PathologyLanguage model17OpennessMedVInT
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 model83OpennessBiomedCLIP
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 model56OpennessRoentGen
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.
ImagingPathology70Openness- 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 M3AE
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 model29OpennessPubMedCLIP
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 model75Openness