Classification of High Risk Prostate Cancer using Deep Learning
Abstract: Prostate cancer is one of the most common types of cancer for men, making proper diagnostic essential. Using machine learning as a tool to help in digital pathology has become increasingly popular and helps to limit the high intra observer variability between pathologists. Due to the many cases of prostate cancer and the large differences between tumours, treatments have to be individualized for each patient. The Active Surveillance was introduced for patients with low risk prostate cancer were treatment in the form of surgery or radiation was deemed too invasive for the cancers current state. Instead the progression is supervised and when, if ever, a certain threshold is surpassed further treatment is discussed. In this thesis it is investigated if a Convolutional Neutral Network (CNN) can be trained to find high risk patients before pathologists can see cancer progression and if benign tissue holds vital information about future development. A CNN was trained on two different datasets, the first containing all of the available data and the second only including the biopsies from the latest examination in a patient's timeline. The results indicate that the problem is hard and the biggest struggle has been to limit the data without introducing new biases. The variability within each class was seemingly large in relation to the possible underlying patterns containing clues about the cancer making the accuracy low. Generalization was overall bad but it was found that when combing the results to make a patient grading, instead of grading individual biopsies, accuracies increased. Peak performance was found when only training on the last biopsies and was for the patient grading 67%. Although no outstanding results were found further research has to be done in the area of predictive prostate cancer classification in order to draw any final conclusions.
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