Classifying the malignancy of prostate lesions from MRI images is crucial in diagnosing prostate cancer at the early stage. In clinical examination, radiologists usually focus on the most salient and distinctive regions to diagnose. However, in many state-of-the-art CNN based methods, the conventional convolution operation extracts the features equally importantly, which leads to an excessive feature learning process on the uninterested regions. To address this challenge, we propose a saliency transfer learning network that allows the model to focus on the salient and influential regions automatically. Moreover, a pyramid central-crop pooling scheme is employed to extract the multi-scale, centric-visual, and salient features from different layers. To validate the effectiveness of the proposed model, extensive experiments are conducted on prostate cancer and non-cancer MRI dataset, the experimental results demonstrate that our proposed model could gain competitive performance (Accuracy 94.9%, Sensitivity 96.7%, Specificity 93.5%, AUC 0.989) on this classification task.

从 MRI 图像中对前列腺病变的恶性程度进行分类对于早期诊断前列腺癌至关重要。在临床检查中，放射科医师通常专注于最突出和最独特的区域进行诊断。然而，在许多最先进的基于 CNN 的方法中，传统的卷积操作同样重要地提取了特征，这导致了对不感兴趣区域的过度特征学习过程。为了应对这一挑战，我们提出了一个显着性迁移学习网络，该网络允许模型自动关注显着和有影响的区域。此外，采用金字塔中心裁剪池方案从不同层提取多尺度、中心视觉和显着特征。为了验证所提出模型的有效性，