H-optimus-1

H optimus 1 (H1) is a state of the art foundation model for computational pathology that turns H&E whole-slide images (WSIs) into high fidelity, task agnostic embeddings, boosting the performance of downstream models in routine and advanced histology workflows, including diagnostics, patient stratification, and prediction of clinical outcomes.

H1 embeddings preserve both cellular detail and tissue level context, making them useful for downstream classifiers and survival models, or as a universal feature backbone in your own pipelines. H1 delivers strong slide level and tile level performance, as well as enhancements to routine analysis like cell counting, typing, and segmentation.

H1 provides scalable, secure embedding generation and model assisted inference that can be integrated into discovery, translational, and clinical research pipelines with enterprise governance. H1 can extract powerful features from histology images for various downstream applications, such as mutation prediction, survival analysis, or tissue classification.

In comparative studies, H1's performance was shown to be superior or highly competitive against other leading pathology foundation models, including UNI2, Virchow2, and Prov GigaPath. Its strong zero shot and few shot learning capabilities underscore the quality of its learned feature representations, allowing it to achieve high accuracy on new tasks with minimal task specific fine tuning. This makes it a powerful and efficient tool for accelerating research and discovery in computational pathology.