PDF(Pubmed)
Abstract：
An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance.
摘要：
自动诊断系统对于帮助放射科医生有效识别大脑异常至关重要。深度学习的卷积神经网络（CNN）算法具有自动特征提取的优势，有利于自动诊断系统。然而,基于CNN的医学图像分类器面临的几个挑战，例如缺乏标签数据和班级不平衡问题，会严重阻碍性能。同时,可能需要多个临床医生的专业知识来实现准确的诊断，这可以反映在多种算法的使用上。在本文中,我们展示了深度堆叠的CNN，基于堆叠泛化的深度异构模型，以利用不同的基于CNN的分类器的优势。该模型旨在当我们没有机会在足够的数据上训练单个CNN时，提高多类脑疾病分类任务的鲁棒性。我们提出了两个层次的学习过程来获得所需的模型。在第一层次,将通过几个过程选择通过迁移学习微调的不同的预训练的CNN作为基本分类器。每个基本分类器都有一个独特的类似专家的字符，这为诊断结果提供了多样性。在第二层,基分类器通过神经网络堆叠在一起，表示最佳组合其输出并生成最终预测的元学习器。当在未触及的数据集上进行评估时，所提出的Deep-StackedCNN获得了99.14%的准确率。该模型显示了其优于同一领域中现有方法的优越性。它还需要更少的参数和计算，同时保持出色的性能。
