Deep Learning for Prostate Cancer Risk Prediction Through Image Analysis of Cells

Abstract: Prostate cancer is one of the most common types of cancer occurring in men. Several types of research have been done using deep learning methods for the classification/prediction of cancer grades. In this thesis, the results of prostate cancer risk prediction, based only on the images of cells from the prostate tissues, have been analyzed. Cell images from the prostate tissues were extracted using a deep learning based segmentation model. These cell images were then used in a Multiple Instance Learning model for cancer risk prediction. An attention mechanism was used to visualize the regions in the tissue to which the model paid more attention. The results suggest that the Multiple Instance Learning (MIL) model achieves an Area Under the receiver Operating Characteristics (AUROC) of 0.641 ± 0.013, which is better than a random model for low-risk vs. high-risk cancer prediction. The model’s prediction was made on cell images, with the glandular information destroyed. The MIL model, however, performs worse than a model which gets to see the glandular architecture of the cells in the prostate tissues.