SOURCE-LINKED INTELLIGENCE

Interpretable Artificial Intelligence across Scales for Next-Generation Cancer Prognostics

CORDIS · observation · Publication date unknown

Interpretable Artificial Intelligence across Scales for Next-Generation Cancer Prognostics Computation pathology has the potential to revolutionize cancer care and research, specifically through improving assessment of patient prognosis and treatment selection by applying advanced machine learning methods to digitized tissue sections, i.e. whole-slide images (WSIs). This will allow us to replace the current state-of-the-art of human-developed cancer grading systems. However, the field is currently hindered by significant knowledge gaps: we do not know how to effectively leverage both global and local information in WSIs, how to identify pan-cancer prognostic features, and how to make machine learning models explainable and interpretable. In this project, I will address these key knowledge gaps by building on the novel stochastic streaming gradient descent developed in my group. Specifically, I will in

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

1494810

unit

EUR