Wisconsin Alumni Research Foundation: SYSTEMS AND METHODS FOR MULTIMODALITY FUSION OF MEDICAL DATA SOURCES

Modern healthcare relies upon a variety of information and diagnostic sources to empower clinicians to make decisions. Cancer diagnosis, treatment, and prognosis is a complex task involving the acquisition of complementary data streams including imaging scans (e.g. CT or MRI images), molecular data (e.g., genome sequencing, gene-expression, epigenomics, also known as multi-omics), cellular-scale (e.g., histological images), as well as clinical data (such as age, gender, cognitive scores). Detailed, comprehensive assessment of the deluge of data is often infeasible for clinicians alone. The recent increase in the performance, costs, and access to sophisticated computational resources has presented an opportunity to supplement the analysis of information by the clinician with algorithmic or deep learning information synthesis.

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