Pathology and disease diagnosis using artificial intelligence and machine learning: Supervised and unsupervised deep-learning based methods on data from medical imaging procedures and patient chart analysis

Machine learning applications in healthcare have shown excellent inroads in medical imaging sciences in recent years. Our research aims to improve upon and open up doors into several different pathology diagnosis applications using artificial intelligence. Contemporary research into some of these applications has shown better diagnostic capacity than expert-level clinicians. Our research into artificial intelligence applications in healthcare include autonomous polyp and bone metastasis pixel-level detection, along with pathology detection in chest x-rays without explicitly labeled data. Through a commercialization research program with Lab2Market, we aim to figure out how exactly our technologies can be improved upon and leveraged to improve clinical outcomes for patients and figure out how we can meet their needs through interacting with our potential customer base.

Faculty Supervisor:

Young-jin Cha

Student:

Partner:

Springboard Atlantic Inc.

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology; Biotechnology; Artificial Intelligence

University:

University of Manitoba

Program: