The purpose of SLDESUTO-BOX is to use unique technology and knowledge within image analysis and based on market demand, develop a Decision Support Toolbox (DST). The DTS will support the pathologists in their challenging task to diagnose and evaluate 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, trained according to different sub-specialties within pathology. Furthermore, we will also build a reference database, enabling pathologist access to verified reference cases with known clinical outcome.
Expected outcome
- An innovative set of products that will provide valuable decision support within routine digital pathology
- Knowledge development within the field of digital pathology image analysis and the integration of technologies in the routine digital pathology workflow.
Planned execution
SLDESUTO-BOX is a joint project between the R&D focused SME, Contextvision AB, in Linköping, Sweden 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 software can then be used by pathologists to contribute to better and more efficient diagnoses. The work packages will be managed in the project using a SCRUM based approach.
Team
- Henning Müller
- Manfredo Atzori
- Oscar Alfonso Jimenez del Toro
- Ivan Eggel
- Roger Schaer
- Sebastian Otalora
Contact
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.
