MSc Thesis Proposal Announcement of Ali Hassan:"Histopathology Classification of Colorectal Cancer WSI using color features with Deep Learning "

 

Date: Wednesday August 18, 2021 

Time:  1:00 pm – 2:30pm 

Meeting URL:  https://zoom.us/j/97684240299?from=addon 

Passcode: if interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca

 

 

Colorectal cancer (CRC) is an emerging global health concern. An average of 73 Canadians will be diagnosed with CRC every day, and another 27 Canadians will lose their life as a result of it. CRC accounts for 12% of all cancer deaths in Canada in the year 2020. Early and accurate diagnosis is vital in saving lives as it significantly influences the length of survival of the patient. Deep learning can be leveraged to aid in the task of identifying cancerous cells within pre-cancerous tissue samples, which are taken from colorectal polyps of patients for CRC screening. In this study, an attempt to improve existing supervised methods of classification of colorectal cancer is made. By revamping/improving the deep learning architecture in ResNet. The network will be trained on a much larger and relevant dataset of colorectal WSI (Whole Slide Image) patches. This study aims to attain better overall accuracy by incorporating color features, which have not been concentrated on in previous studies. All while retaining similar performance as compared to existing state-of-the-art methods of CRC classification. 

 

Keywords: Deep Learning, Classification, CNN, ResNet, Whole Slide Images

 

Internal Reader: Dr. Imran Ahmad     

External Reader: Dr. Faouzi Gherib 

Advisor:  Dr. Boubakeur Boufama