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Deep Learning Approaches to Automated Phenotypic Profiling

Quantifying cellular phenotypes is the key to all cell biology studies. However, modern imaging techniques can easily generate more data than an average user can comfortably handle. In this presentation, Dr. Chao discusses two deep learning approaches, one semi-supervised and one supervised, for building image analysis pipelines. Either approach can be run on a free cloud GPU instance.

Presenter: Jesse Chao, PhD
Scientist, Sunnybrook Research Institute

Jesse completed his PhD at the University of British Columbia (UBC) in cell biology and genomics. He then continued his training at the University of California, San Diego. After, he switched his focus to developing machine learning approaches for assessing the physiological impacts of genetic variants associated with hereditary cancer at UBC. During this time, he started to develop deep learning approaches to automated phenotypic profiling based on high-content imaging data.

Deep Learning Approaches to Automated Phenotypic Profiling

Quantifying cellular phenotypes is the key to all cell biology studies. However, modern imaging techniques can easily generate more data than an average user can comfortably handle. In this presentation, Dr. Chao discusses two deep learning approaches, one semi-supervised and one supervised, for building image analysis pipelines. Either approach can be run on a free cloud GPU instance.

Presenter: Jesse Chao, PhD
Scientist, Sunnybrook Research Institute

Jesse completed his PhD at the University of British Columbia (UBC) in cell biology and genomics. He then continued his training at the University of California, San Diego. After, he switched his focus to developing machine learning approaches for assessing the physiological impacts of genetic variants associated with hereditary cancer at UBC. During this time, he started to develop deep learning approaches to automated phenotypic profiling based on high-content imaging data.

Experts
Jesse Chao
Scientist
Sunnybrook Research Institute

Jesse completed his Ph.D. at the University of British Columbia (UBC) in cell biology and genomics. He then continued his training at the University of California, San Diego. After, he switched his focus to developing machine learning approaches for assessing the physiological impacts of genetic variants associated with hereditary cancer at UBC. During this time, he started to develop deep learning approaches to automated phenotypic profiling based on high-content imaging data.

Deep Learning Approaches to Automated Phenotypic ProfilingDec 07 2021
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