Lectures and seminars Clinicum seminar: AI-based computational pathology: enabling scalable and comprehensive phenotyping for patient stratification in cancer precision medicine

Precision medicine has the potential to substantially improve cancer patient outcomes. However, to be effective, precision diagnostic solutions for patient stratification are required. To improve outcomes for broad groups of patients, fast, reliable and cost-effective solutions are needed. Current routine diagnosis of cancer is based on manual histopathological assessment, which is imprecise. Molecular diagnostics on the other hand offers improved patient stratification, but at a high price, limiting patient access and imposing a high economic burden on healthcare systems. 

We develop, validate, translate and implement AI-based histopathology image analysis solutions (computational pathology) for image-based phenotyping and patient stratification, both in the clinical setting and for cancer research. Clinical pathology is currently undergoing a digital transition, which facilitates implementation of AI-based decision support tools for precision diagnostics that are fast and only cost a fraction compared with molecular diagnostics. Our research is based on applications of deep learning -based methods to model large population representative studies with gigapixel histopathology images, registry-based clinical information, and molecular profiling data. In this seminar Mattias Rantalainen will provide an overview of their on-going research, recent results and translational activities in the area of breast cancer.

This seminar will be held in English.

It will a hybrid seminar.  At Clarence Crafoord, Karolinska Universitetssjukhuset Solna. Eugeniavägen 3, Hiss A område 04 and on Zoom.