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【High-Dimensional Statistical Modeling Team】
【Speaker】 Mr. Peter Jack NAYLOR
Predicting from very large images: Application to treatment response in triple-negative breast cancer.
The rise of digital pathology and with it the challenges of histopathology analysis have been the focus of a worldwide effort in the overall fight against cancer. In parallel, the recent success of automated decision-making, machine learning, and specifically deep learning, have revolutionized the basis of research as we know today. We tackle the prediction of treatment response in triple-negative breast cancer patients with two different approaches that reach similar outcomes. The first line of approach, based on the recent success of computer vision, extracts learned features from the data in order to perform classification. The second line of approach forces the information flow to pass through nuclei segmentation.
In particular, it allows the incorporation of biologically relevant high-resolution information on to a lower resolution overview.