Handling and Processing Big Data for Biomedical Discovery with MATLAB
Dr. Raphael Thierry, Friedrich Miescher Institue for Biomedical Research
Like many other disciplines, the biomedical and pharmaceutical industry experienced an exponential growth of their data size over the last decade. Indeed, automated microscopes for high-throughput screening and newly developed microscopes all produce terabytes of data within minutes. One promising approach consists of using machine learning techniques to generate classifiers that categorize subjects into multiple classes based on attributes measured in each subject. An obvious use of such classifiers lies in the potential to analyze large biomedical data sets in a highly effective and objective manner. These techniques create classifiers out of multiple features, some extracted from different imaging modalities, sometimes supervised by the expert annotations of biologists to infer models. Thus, they are inherently better in detecting and assessing phenotypes or disease progression and represent a dedicated tool for data analysis when robust and accurate segmentation is required to cope with the variability of phenotypes, biological specimens, and microscopic setup. This presentation illustrates the experience gathered over the last 5 years on various state-of-the-art machine learning techniques applied to biomedical image processing
Recorded: 23 Jun 2016
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