Seminar: Using machine learning approach to scoring the expression status of the biomarkers in breas
Thesis Proposal Presentation
M.Sc. Thesis Proposal
Supervisor: Dr. Jian Tang
Using machine learning approach to scoring the expression status of the biomarkers in breast cancer
Department of Computer Science
Friday, November 22, 2013, l1:00 a.m., Room EN 2022
In breast cancer, biomarkers such as hormone receptors play a pivotal role in patient management. In current practice, hormone receptors, generally, are scored by pathologists by eyeballing the percentage of tumor cells with the hormone receptors. This is inherently somewhat subjective, and thus likely less reproductive than an automated technique. The aim of this study is to use a machine learning approach to automatically interpret the hormone receptor status in breast cancer cases. The goal is to increase reproducibility of results, to allow better study of the different variables that affect hormone receptor testing. Ultimately, this should facilitate a more accurate selection of the cases that would benefit from targeted therapies, thus making the best use of limited health care resources.