基于密集卷积网络的X线气胸检测与定位

现有X线气胸检测存在两个主要问题：一是由于气胸通常与肋骨、锁骨等组织重叠,在临床上存在较大的漏诊,而现有算法的检测性能仍有待提高;二是现有基于卷积神经网络的算法无法给出可疑的气胸区域,缺乏可解释性。针对上述问题,提出了一种结合密集卷积网络（DenseNet）与梯度加权类激活映射的方法用于X线气胸的检测与定位。首先,构建了一个较大规模的胸部X线数据集PX-ray用于模型的训练和测试。其次,修改DenseNet的输出节点并在全连接层后添加一个sigmoid函数对胸片进行二分类（气胸/非气胸）。在训练过程中通过设置交叉熵损失函数的权重来缓解数据不平衡问题,提高模型准确率。最后,提取网络最后一个卷积层的参数以及对应的梯度,通过梯度加权类激活映射算法获得气胸类别的粗略定位图。在PX-ray测试集上的实验结果表明,所提方法的检测准确率为95.45%,并且在曲线下面积（AUC）、敏感度、特异性等指标上均高于0.9,优于VGG19、GoogLeNet以及ResNet算法,同时实现了对气胸区域的可视化。

Pneumothorax detection and localization in X-ray images based on dense convolutional network

There are two main problems about pneumothorax detection in X-ray images. The pneumothorax usually overlaps with tissues such as ribs and clavicles in X-ray images, easily causing missed diagnosis and the performance of the existing pneumothorax detection methods remain to be improved. The suspicious pneumothorax area detection cannot be exploited by the convolutional neural network-based algorithms, lacking the interpretability. Aiming at the problems, a novel method combining Dense convolutional Network (DenseNet) and gradient-weighted class activation mapping was proposed. Firstly, a large-scale chest X-ray dataset named PX-ray was constructed for model training and testing. Secondly, the output node of the DenseNet was modified and a sigmoid function was added after the fully connected layer to classify the chest X-ray images. In the training process, the weight of cross entropy loss function was set to alleviate the problem of data imbalance and improve the accuracy of the model. Finally, the parameters of the last convolutional layer of the network and the corresponding gradients were extracted, and the areas of the pneumothorax type were roughly located by gradient-weighted class activation mapping. The experimental results show that, the proposed method has the detection accuracy of 95.45%, and has the indicators such as Area Under Curve (AUC), sensitivity, specificity all higher than 0.9, performs the classic algorithms of VGG19, GoogLeNet and ResNet, and realizes the visualization of pneumothorax area.