Development of Artificial Intelligence Powered Technologies in Computational Pathology to Enable Automated Slide Screening in Whole Slide Imaging – Year two

Advances in Whole Slide Imaging (WSI) and Machine Learning (ML) open new opportunities to create innovative solutions in healthcare and in particular digital pathology to increase efficiencies, reduce cost and most importantly improve patient care. This project envisions the creation of new automated ML tools including the design of a custom Convolution Neural Network (CNN) architecture for whole slide imaging in digital pathology. The custom CNN will be trained to learn different representations of histology tissues so that it can separate healthy from diseased tissues. A substantial database of labeled healthy tissue will be used to assess the performance of the proposed solution. A limited validation of the engineering prototype developed through this project will take place at St. Michael’s hospital in Toronto. The technologies developed through this project have the potential to be integrated in an automated screening process in pathology to improve pathologist time efficiencies and reduce errors in diagnosis of disease. The outcome of this research will be of great benefit to the industrial partner since it serves as a pilot project for developing an advanced, data-driven, digital pathology solution that complements its current line of pathology scanners.

Faculty Supervisor:

Konstantinos N Plataniotis

Student:

Mahdi S Hosseini

Partner:

Huron Digital Pathology

Discipline:

Engineering - computer / electrical

Sector:

University:

University of Toronto

Program: