MSc Thesis Defense Announcement by Ali Hassan:"Histopathology Classification of Colorectal Cancer Whole Slide Images Using Color Features with Deep Residual Transfer Learning"

Abstract: 
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 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 identifying cancerous cells within pre-cancerous tissue samples, which are taken from colorectal polyps of patients for CRC screening. This study attempts to improve existing supervised classification methods of colorectal cancer by revamping/improving the deep learning architecture in ResNet. The network will be trained on a much larger, 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 compared to existing state-of-the-art methods of CRC classification. Four network models are applied to a large histopathological dataset. All network models are variations of Residual networks at multiple depths. The best results are attained using a pre-trained ResNet-50 model. The overall results show that the residual network performs similarly to the much deeper DenseNet-121 model and better than the cell level framework described in a previous study. The ResNet-50 attains values of ResNet-50 achieved 88.58%, 92.04%, 81.86%, 86.65% for Accuracy, Precision, Recall and F1-Score respectively.

Keywords: Residual Learning, Transfer Learning, Deep Learning, Residual Networks

MSc Thesis Committee: 
Internal Reader: Dr. Imran Ahmad
External Reader: Dr. Faouzi Gherib
Advisor: Dr. Boubakeur Boufama
Chair: Dr. Shafaq Khan

 

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