Vector Scholarship Winner Rachel Theriault Recognized for AI in Cancer Analysis
Rachel Theriault, a Vector Scholarship recipient and graduate student at Queen’s University, has been recognized for her innovative application of artificial intelligence to breast cancer analysis. Her undergraduate thesis, which utilized sparse subspace clustering to detect cancer from DESI-MS scans, ranked in the top ten percent of the Global Undergraduate Awards Programme in Computer Science. Theriault’s research aims to assist pathologists in distinguishing between cancerous and benign tissues during lumpectomies, potentially reducing the need for second surgeries, which currently occur in over 20% of cases due to delayed or incomplete initial analysis. By applying machine learning algorithms typically used in facial recognition to high-dimensional mass spectrometry data, she addresses the complexity of heterogeneous cancer tissues. Supported by a $17,500 Vector Scholarship, Theriault is now expanding her master’s research to include data visualization techniques and the analysis of skin, liver, and prostate cancers. Her work highlights the growing intersection of AI and healthcare, demonstrating how computational strategies can improve diagnostic speed and accuracy, ultimately enhancing patient outcomes through collaborative efforts among surgeons, chemists, and computer scientists.
Wire timeline
Vector Scholarship Winner Rachel Theriault Recognized for AI in Cancer Analysis
Rachel Theriault, a Vector Scholarship recipient and graduate student at Queen’s University, has been recognized for her innovative application of artificial intelligence to breast cancer analysis. Her undergraduate thesis, which utilized sparse subspace clustering to detect cancer from DESI-MS scans, ranked in the top ten percent of the Global Undergraduate Awards Programme in Computer Science. Theriault’s research aims to assist pathologists in distinguishing between cancerous and benign tissues during lumpectomies, potentially reducing the need for second surgeries, which currently occur in over 20% of cases due to delayed or incomplete initial analysis. By applying machine learning algorithms typically used in facial recognition to high-dimensional mass spectrometry data, she addresses the complexity of heterogeneous cancer tissues. Supported by a $17,500 Vector Scholarship, Theriault is now expanding her master’s research to include data visualization techniques and the analysis of skin, liver, and prostate cancers. Her work highlights the growing intersection of AI and healthcare, demonstrating how computational strategies can improve diagnostic speed and accuracy, ultimately enhancing patient outcomes through collaborative efforts among surgeons, chemists, and computer scientists.
Vector Institute for Artificial Intelligence