All Competitors

A biomedical foundation model for joint segmentation, detection, and recognition across nine imaging modalities using natural language prompts.

A 1.1B parameter open-source vision transformer for histopathology, trained on 500,000+ H&E whole slide images from 4,000 clinical practices worldwide.

Training-free cryo-ET tomogram segmentation that adapts SAM and DINOv2 for 3D volumetric data, enabling full tomogram segmentation from a single user prompt.

DINOv2-based Vision Transformer foundation models for digital pathology, trained on over 1 million whole-slide images. Available as Hibou-B (86M) and Hibou-L (307M) under Apache 2.0.

A suite of virtual staining models that translate label-free microscopy images into fluorescent-equivalent staining of nuclei and plasma membranes.

Self-supervised pathology foundation model (ViT-L/16, DINOv2) pretrained on 100M+ H&E tiles from 100,000+ whole-slide images. State-of-the-art on 34 pathology tasks.

Universal cell segmentation model adapting Meta's SAM for biology. Segments mammalian cells, yeast, and bacteria across diverse imaging modalities with human-level accuracy.

Vision Transformer for cell instance segmentation and classification in H&E digital pathology, extended by CellViT++ with foundation model backbones and few-shot adaptation.