Digital Pathology

ContextVision is developing new innovative decision support products for digital pathology, and a research project, SL-DESUTO, was started in 2015 to accomplish this.

The project will use unique technology and knowledge within image analysis and Deep Learning and, based on market demand, will develop a Decision Support Toolbox (DST). The DST will support pathologists in their challenging task of diagnosing and evaluating the prognosis of different types of cancer. The DST will consist of a variety of tools that will be based on learning models for image analysis and trained according to different sub-specialties within pathology. Furthermore, the intention is to create a reference database, using unique image search tools, to enable pathologists to access verified reference cases with known clinical outcomes.

The research program was initially focusing on prostate cancer, and since July 2016, is also including breast cancer. The product portfolio will include decision support tools for several cancer diseases, and the first product is expected to reach the market during 2018.

The SLDESUTO project

This is a joint project between ContextVision AB and the eHealth unit of HES-SO, University of Applied Sciences Western Switzerland in Sierre, Switzerland. State-of-the-art machine learning algorithms, including Deep Learning, will be used to train the software to automatically recognize, identify and classify abnormal patterns in the digital images with a variety of pathologies.
“This project has received funding from the Eurostars-2 Joint Program with co-funding from the European Union’s Horizon 2020 research and innovation program”.

More about the project here

Advisory Board

In order to ensure the highest possible international standard of the research project and to strategically guide the product development an Advisory Board has been created, including:

The members represent extensive competence and experience within pathology with focus on digital pathology as well as within machine learning with focus on deep learning.

Press-releases: