论文标题
胸部疾病分类的总体方法
An Aggregate Method for Thorax Diseases Classification
论文作者
论文摘要
在现实词的医学图像分类中发现的一个常见问题是数据集正面和负面模式中正面模式通常很少见的固有失衡。此外，在与神经网络的多个类别的分类中，训练模式被视为一个输出节点中的正模式，而在所有其余的输出节点中，训练模式被视为阴性。在本文中，损失功能中训练模式的权重不仅基于同类课程的训练模式的数量，而且还基于不同节点的训练模式的数量，其中其中一个将这种训练模式视为正面，而其他节点则将其视为负面。我们提出了一种对深度网络培训的权重计算算法的组合方法，以及从最先进的胸腔疾病分类问题的最先进的深层网络体系结构进行的培训优化。胸部X射线图像数据集的实验结果表明，这种新的加权方案改善了分类性能，并且来自EditiveNet的训练优化也改善了性能。我们将骨料方法与先前的胸部疾病分类研究中的几种表现进行了比较，以提供与所提出方法的公平比较。
A common problem found in real-word medical image classification is the inherent imbalance of the positive and negative patterns in the dataset where positive patterns are usually rare. Moreover, in the classification of multiple classes with neural network, a training pattern is treated as a positive pattern in one output node and negative in all the remaining output nodes. In this paper, the weights of a training pattern in the loss function are designed based not only on the number of the training patterns in the class but also on the different nodes where one of them treats this training pattern as positive and the others treat it as negative. We propose a combined approach of weights calculation algorithm for deep network training and the training optimization from the state-of-the-art deep network architecture for thorax diseases classification problem. Experimental results on the Chest X-Ray image dataset demonstrate that this new weighting scheme improves classification performances, also the training optimization from the EfficientNet improves the performance furthermore. We compare the aggregate method with several performances from the previous study of thorax diseases classifications to provide the fair comparisons against the proposed method.
