Soft-label reinforced rtCAB for guided prostate tissue sampling

In this paper a real-time computer-aided biopsy (rtCAB) system is presented to support prostate cancer diagnosis. Different types of features are extracted from trans-rectal ultrasound data and an ensemble learning algorithm is used in classification phase. A new label assignment method is also employed to provide soft or crisp class labels for uncertain data. The proposed model could be implemented in parallel on GPU using CUDA platform to provide real-time support to physician during biopsy. Experiments on ground truth images from biopsy finding demonstrate that the proposed approach can properly deal with uncertain data and is able to provide better results than some examined supervised and semi-supervised classifiers.