TileBio transforms whole‑slide histopathology images into an AI‑learned visual language, representing each slide as a structured document of tissue "tokens." This self‑supervised foundation model, trained on over a million unlabelled slides, enables large language model techniques to detect patterns beyond human perception, improving diagnostic precision and supporting biomarker discovery. The platform offers an API for clinical labs, researchers, and pharmaceutical teams to integrate advanced tissue analysis into diagnostics, research, and drug development workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Pathology diagnosis relies on visual assessment of tissue slides, but many microscopic patterns are beyond human perception. A global shortage of pathologists and the high cost of manually labeled datasets limit the scalability and accuracy of AI tools, leaving vast amounts of imaging data untapped for clinical and research use.
Solution
TileBio converts whole‑slide histopathology images into an AI‑learned visual language, representing each slide as a structured document. This enables the application of large language model techniques to interpret complex tissue contexts and relationships. The self‑supervised approach trains on millions of unlabelled samples, extracting patterns that surpass human detection and improving diagnostic precision. By scaling a foundation model across diverse pathology data, TileBio provides a universal, interpretable representation that can be leveraged for early disease detection, biomarker discovery, and drug development acceleration.
Target Audience
Primary users include clinical pathology laboratories, biomedical researchers, and pharmaceutical companies seeking AI‑enhanced diagnostic insights and biomarker discovery.
Features
- Self‑supervised training on over 1 million whole‑slide images without the need for manual annotations
- Generation of “language tokens” that encode tissue morphology, allowing integration with large language model pipelines
- Proprietary large language model that interprets slide‑level documents to identify disease patterns and relationships
- Scalable architecture supporting multiple cancer types and adaptable to new pathology domains
- API for seamless integration into clinical diagnostics, research workflows, and pharmaceutical pipelines