American Statistical Association
New York City
Metropolitan Area Chapter

Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
Biostatistics Seminar Series

Thomas Fuchs, Ph.D.
Department of Medical Physics
Memorial Sloan Kettering Cancer Center


We are in the midst of a data-driven revolution in the biomedical sciences. New imaging modalities, deep sequencing and mass-spectrometry lead to an explosion of data in molecular biology, medical research and clinical practice which is not graspable for single human experts any more. Machine learning gives us the capabilities to gain knowledge from these vast amounts of data and it hands us the tools to tackle a multitude of research questions.

The field of pathology is particularly exciting for statistical learning:

In this talk I will demonstrate how we can train ensemble classifiers based on low-level cues from terabytes of whole slide image data and how this information is utilized in a generative top-down model to understand tissue morphology and its impact on the survival of cancer patients. In particular, I will focus on a novel decision forest model for image understanding and a new sampling technique for approximate Bayesian computation to estimate the parameters of spatial point processes.

These recent breakthroughs in large scale machine learning provide the enabling techniques for applications in Computational Pathology ranging from protein quantification over survival analysis to clinical decision support.

Date: Wednesday, January 18, 2017
Time: 4:00 - 5:00 P.M.
Location: Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
485 Lexington Avenue
(Between 46th & 47th Streets)
2nd Floor, Conference Room B
New York, New York

**Outside visitors please email for building access.
You must be on the security list to enter the floor.

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