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Optimizing QUS-based ML Training Pipeline Efficiency for Liver Health Analysis
This project will involve developing tools and conducting analyses to improve the efficiency and quality of a quantitative ultrasound (QUS)-based machine learning pipeline. The student will work within a company that uses raw ultrasound image data captured on mobile devices to produce liver health scores (measured in kPa). These scores are used to assess liver stiffness, an indicator of liver fibrosis and related health issues.
Faculty Supervisor:
Jean-Baptiste Poline
Student:
Partner:
Oncoustics
Discipline:
Engineering
Sector:
Manufacturing; Professional, scientific and technical services
University:
McGill University
Program:
Business Strategy Internship
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