Ultrasound-Based Detection of Prostate Cancer Using Automatic Feature Selection with Deep Belief Networks

We propose an automatic feature selection framework for analyzing temporal ultrasound signals of prostate tissue. The framework consists of: 1) an unsupervised feature reduction step that uses Deep Belief Network (DBN) on spectral components of the temporal ultrasound data; 2) a supervised fine-tuning step that uses the histopathology of the tissue samples to further optimize the DBN; 3) a Support Vector Machine (SVM) classifier that uses the activation of the DBN as input and outputs a likelihood for the cancer. In leave-one-core-out cross-validation experiments using 35 biopsy cores, an area under the curve of 0.91 is obtained for cancer prediction. Subsequently, an independent group of 36 biopsy cores was used for validation of the model. The results show that the framework can predict 22 out of 23 benign, and all of cancerous cores correctly. We conclude that temporal analysis of ultrasound data can potentially complement multi-parametric Magnetic Resonance Imaging (mp-MRI) by improving the differentiation of benign and cancerous prostate tissue.