Abstract:  AI driven by deep learning has attracted much attention in the last decade. The enormous success of deep learning stems from its unique capabilities of extracting essential features from big data and then making inferences. However, the data-driven process has many potentialflaws, such as the demand for a large amount of annotated data and lack of interpretability. In this talk, I will summarize recent advances in deep learning-imaging techniques, in particular, unsupervised and semi-supervised deep learning as well as technical tools that make AI more interpretable and trustworthy. Specific applications of the new generation of deep learning techniques in biomedical imaging, treatment planning, high dimensional genomic data analysis, and clinical decision-making will be discussed. 

Websites:

http://xinglab.stanford.edu