Processing Tissue Micro-Array Images Using Machine Learning Techniques as Preparation for Determining Gleason Grade of Prostate Cancer

Abstract

Prostate Cancer (PCa) is the sixth most common and the second deadliest cancer among men in the world. While the diagnosis of PCa mostly depends on the decisions of pathologists based on clinical symptoms of the patients and their careful observations upon the patients' prostate biopsy, in particular, the biopsy of the patients' glands. Often there are disagreements and debates among pathologists in deciding the Gleason grade of the same biopsy samples. Majority votes take place to avoid disputes. In this research, we aim to find an automatic method to facilitate the process of determining Gleason grade of PCa. In order to achieve that, we explore both Support Vector Machine (SVM) and Convolutional Neural Networks (CNNs) in order to determine whether the local patches of the Tissue Micro-Array (TMA) images contain glands, border region of glands, or stroma, and it turns out that with limited amount of labelled data, SVM outperforms the other. With the results from classifiers, we can filter irrelative portions of images. This processed TMA images would allow pathologists to focus on the locations of glands existing potentially. Moreover, with processed images, the next level of training – predicting the Gleason grade of PCa and generating corresponding images under certain Gleason grade – using models like CNNs and Generative Adversarial Networks (GANs) is expected to be much faster.