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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 97–120 of 386 filtered models
Micronuclei instance segmentation for fluorescence microscopy, using an anchor-tuned Mask R-CNN to detect micronuclei and link them to parent nuclei.
Neuro-oncology foundation model for brain tumor MRI, using distributionally robust pretraining for molecular subtyping and survival prediction.
Centrosome segmentation framework chaining YOLOv11 detection, U-Net refinement, and StarDist cell boundaries across immunofluorescence and IHC tissue.
Self-supervised foundation model for 3D brain MRI, learning transferable anatomical representations from unlabeled scans for disease classification.
Cryo-EM and cryo-ET map enhancement model that sharpens density maps with a Mamba-based dual-branch UNet and local resolution-guided learning.
Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.
Image-to-image translation from label-free phase-contrast microscopy to H&E-like images, so pretrained histopathology models run on live cells.
Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.
Histopathology classifier separating atypical from normal mitotic figures, LoRA-adapting a DINOv3 vision transformer with 1.3M trainable parameters.
Generalist MRI vision-language foundation model that handles reconstruction, segmentation, abnormality detection, and report generation in one model.
Low-dose CT denoising across dose levels and body regions from one checkpoint, conditioning residual diffusion on learned dose and anatomy embeddings.
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.
Retinal OCT vision-language model that writes layer-by-layer clinical summaries and assigns six-class disease labels from a single B-scan.
Red blood cell morphology foundation model pretrained on 1.25 million single-cell crops, released as small, base, and large ViT feature extractors.
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.