Cancer is a complex disease; its different causes and types strongly affect patient treatment and prognosis. Through exploring and developing novel techniques for multimodal information fusion we aim to improve understanding of the disease, its causes and progression, and enable reliable early detection and confident differentiated diagnosis, thereby providing a solid basis for treatment planning. In this collaboration between Uppsala University and Center for Clinical Research Dalarna we will, through powerful AI-based data fusion, combine information from a range of imaging techniques to capture complementary information about a specimen.

We hypothesize that fusion of heterogeneous information about the specimen will be beneficial in a number of ways, enabling

• improved and differentiated ground truth data for learning and evaluation, where additional modalities support the cytopathologist towards more reliable annotation;
• improved performance of our existing AI-based cancer diagnostics decision support system, through (direct or indirect) use of relevant information from additional modalities;
• increased explainability, through ability of the system to indicate and correlate active virus infections with the cancer;
• improved understanding of the disease and its causes, enabling improved patient treatment (personalized medicine).