Image Processing and Deep Neural Networks for Detection of Immune Cells on Histological Images of Bladder Cancer
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- Studentoppgaver (TN-IDE) 
Bladder cancer is the tenth most common cancer type, where urothelial carcinoma is the most common type of bladder cancer. Bladder cancer has been classified as the most expensive type of cancer per patient, as the need for post-treatment monitoring often lasts the rest of the patient’s life. A pathologist needs to diagnose and evaluate the risk of progression and relapse from analyzing histological images. Recent research shows a correlation between the number of regulatory T-cells and which patients that get progression to a higher cancer grade. Today a computer randomly picks out a sub-set of cells, that is to be manually counted and classified; this will serve as an estimation for regulatory T-cells compared to other cells. This paper proposes a more automated solution to aid in analyzing histological images for the number of regulatory T-cells and other cells present. The two proposed systems are using classical image processing to find and classify the cells based on color and using a convolutional neural network to detect and classify smaller parts of the images. Both systems will attempt to estimate the number of regulatory T-cells compared to other cells. The classical image processing had an underestimation of 4.7% for regulatory T-cells while having a 4.5% overestimation of other cells. The convolutional neural network showed a correlation between the number of classifications and the actual amount of cells but requires further work to be usable.
Master's thesis in Automation and signal processing