Before revision: Data scarcity has refrained deep learning models from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature.
After revision: Deep learning models have had a great success in disease classifications using large data pools of skin cancer images or lung X-rays. However, data scarcity has been the roadblock of applying deep learning models from making greater progress in prostate images analysis using multiparametric MRI . In this paper, an efficient convolutional neural network (CNN)was developed to classify lesion malignancy for prostate cancer patients, based on which model interpretation was systematically analyzed to bridge the gap between natural images and MR images, which is the first one of its kind in the literature.