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 directly on prostate multiparametric MRI (mpMRI). Although model interpretation has been heavily studied for natural images for the past few years, there has been a lack of interpretation of deep learning models trained on medical images. In this paper, an efficient convolutional neural network (CNN) was developed and the model interpretation at various convolutional layers was systematically analyzed to improve the understanding of how CNN interprets multimodality medical images and the predictive powers of features at each layer. The problem of small sample size was addressed by feeding the intermediate features into a traditional classification algorithm known as weighted extreme learning machine (wELM), with imbalanced distribution among output categories taken into consideration.

中文翻译：

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使用基于卷积神经网络的多参数MRI深入了解肿瘤灶的分类。

深度学习模型使用皮肤癌图像或肺部X射线的大型数据池在疾病分类中取得了巨大成功。但是，数据稀缺一直是直接在前列腺多参数MRI（mpMRI）上应用深度学习模型的障碍。尽管过去几年对自然图像进行了模型解释的大量研究，但仍缺乏对在医学图像上训练的深度学习模型的解释。在本文中，开发了一种高效的卷积神经网络（CNN），并系统地分析了各个卷积层的模型解释，以增进对CNN如何解释多模态医学图像以及每一层特征的预测能力的了解。