Interstitial lung diseases are a large group of heterogeneous diseases characterized by different degrees of alveolitis and pulmonary fibrosis. Accurately diagnosing these diseases has significant guiding value for formulating treatment plans. Although previous work has produced impressive results in classifying interstitial lung diseases, there is still room for improving the accuracy of these techniques, mainly to enhance automated decision-making. In order to improve the classification precision, our study proposes a convolutional neural networks-based framework with additional information. Firstly, ILD images are added with their medical information by re-scaling the original image in Hounsfield Units. Secondly, a modified CNN model is used to produce a vector of classification probability for each tissue. Thirdly, location information of the input image, consisting of the occurrence frequencies of different diseases in the CT scans on certain locations, is used to calculate a location weight vector. Finally, the Hadamard product between two vectors is used to produce a decision vector for the prediction. Compared to the state-of-the-art methods, the results using a publicly available ILD database show the potential of predicting these using different additional information.
翻译:肺部间歇性疾病是多种疾病,其特征是不同程度的肺炎和肺纤维化。准确诊断这些疾病对于制定治疗计划具有重要的指导价值。虽然先前的工作在对肺部间疾病进行分类方面产生了令人印象深刻的结果,但是仍有改进这些技术准确性的余地,主要是为了提高自动化决策。为了提高分类精确度,我们的研究提出了一个基于神经网络的革命性框架,并提供了补充信息。首先,通过对Hounsfield单位的原始图像进行重新缩放,在医疗信息中添加了ILD图像。第二,使用了经过修改的CNN模型来产生每个组织分类概率的矢量。第三,输入图像的定位信息,包括某些地点CT扫描中不同疾病的发生频率,用来计算一个位置加权矢量。最后,两个矢量之间的Hadamard产品被用来产生一种用于预测的决策矢量。与最新方法相比,使用可公开获得的ILD数据库的结果显示了使用这些不同信息预测的可能性。