Evident LogoOlympus Logo

Ask the Experts

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 Profiling2024년3월28일
죄송합니다. 이 페이지는 해당 국가에서 사용할 수 없습니다.
Ask the Expert Sign-up

By clicking subscribe you are agreeing to our privacy policy which can be found here.

Sorry, this page is not
available in your country.

죄송합니다. 이 페이지는 해당 국가에서 사용할 수 없습니다.