Selecting proper combination of mpMRI sequences for prostate cancer classification using multi-input convolutional neuronal network.

Abstract

This study aims to develop a deep-learning-basedmethod to classify clinically significant (CS) and clinically insignificant (CiS) prostate cancer (PCa) on multiparametric magnetic resonance imaging (mpMRI) automatically, and to select suitable mpMRI sequences for PCa classificationin different anatomic zones. A multi-input selection network (MISN) is proposed for both PCa classification and theselection of the optimal combination of sequences for PCa classification in a specific zone.MISN is a multi-input/-output classification network consisting of nine branches to process nine input images from the mpMRI data. To improve classification accuracy and reduce model parameters, a pruning strategy is proposed to select a subset of the nine branches of MIST to form two more effective networks for the peripheral zone (PZ) PCa and transition zone (TZ) PCa, which are named as PZN and TZN, respectively. Besides, a new penalized cross-entropy loss function is adopted to train the networks tobalance the classification sensitivity and specificity. The proposed methods were evaluated on the PROSTATEx challenge dataset and achieved an area under the receiver operator characteristics curve of 0.95, which was much higher than currently published results and ranked first out of more than 1500 entries submitted to the challenge at the time of submission of this paper. For PZ-PCa and TZ-PCa classification, PZN and TZN achieved better performance than MISN. Higher performance can be achieved by selecting a suitable subset of the mpMRI sequences in PCa classification.