Katana Labs develops a cloud-based platform that utilizes AI for the image analysis of tumor tissue, enabling precise detection and classification of cancer cells in histopathological diagnostics. This technology enhances the efficiency and accuracy of medical image data analysis for pathology labs and research teams in the pharmaceutical and biotech sectors.
Funding
$600K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
QFounders
Product
Problem
Pathology labs face increasing workloads and shortages of skilled personnel, creating bottlenecks in cancer diagnostics. Traditional manual microscopy for tumor tissue analysis is time-consuming, subjective, and prone to inter-observer variability, potentially delaying accurate diagnoses and treatment decisions.
Solution
Katana Labs offers a cloud-based, AI-powered image analysis platform designed to accelerate and enhance cancer diagnostics for clinical pathology and biotech R&D workflows. The platform, called PAIKON, integrates with existing lab systems and uses AI algorithms to automatically detect, classify, and quantify cells and nuclei in whole slide images. This enables pathologists to analyze entire tissue samples in seconds, providing more comprehensive information for decision-making while reducing manual effort and improving diagnostic accuracy. The platform's cloud infrastructure, hosted on German servers, ensures scalability, security, and ease of access.
Target Audience
The primary target audience includes mid-market pathology lab suppliers, pathology labs, and research teams in the pharmaceutical and biotech sectors.
Features
- Digital microscope interface accessible via web browser for whole slide image analysis
- AI-powered detection and classification of cells and nuclei within tissue samples
- Automated counting and measurement of interactions between selected gene signals
- Cloud-based infrastructure hosted on German servers for secure and scalable processing
- Seamless integration with existing laboratory information systems (LIS)
- AI algorithms developed with pathologists to ensure ease-of-use and workflow integration