Objective

Using the deep learning model to realize tongue image-based disease location recognition and focus on solving two problems: 1. The ability of the general convolution network to model detailed regional tongue features is weak; 2. Ignoring the group relationship between convolution channels, which caused the high redundancy of the model.

Methods

To enhance the convolutional neural networks. In this paper, a stochastic region pooling method is proposed to gain detailed regional features. Also, an inner-imaging channel relationship modeling method is proposed to model multi-region relations on all channels. Moreover, we combine it with the spatial attention mechanism.

Results

The tongue image dataset with the clinical disease-location label is established. Abundant experiments are carried out on it. The experimental results show that the proposed method can effectively model the regional details of tongue image and improve the performance of disease location recognition.

Conclusion

In this paper, we construct the tongue image dataset with disease-location labels to mine the relationship between tongue images and disease locations. A novel fully-channel regional attention network is proposed to model the local detail tongue features and improve the modeling efficiency.

Significance

The applications of deep learning in tongue image disease-location recognition and the proposed innovative models have guiding significance for other assistant diagnostic tasks. The proposed model provides an example of efficient modeling of detailed tongue features, which is of great guiding significance for other auxiliary diagnosis applications.

中文翻译：

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用舌图像进行疾病位置识别的全通道区域注意力网络

客观的

利用深度学习模型实现基于舌头图像的疾病位置识别，重点解决两个问题：1.一般卷积网络对细节区域舌头特征建模能力弱；2. 忽略了卷积通道之间的分组关系，导致模型冗余度高。

方法

增强卷积神经网络。在本文中，提出了一种随机区域池化方法来获得详细的区域特征。此外，还提出了一种内成像通道关系建模方法来对所有通道上的多区域关系进行建模。此外，我们将其与空间注意力机制相结合。

结果

建立带有临床疾病位置标签的舌头图像数据集。对其进行了大量的实验。实验结果表明，所提出的方法能够有效地对舌头图像的区域细节进行建模，提高疾病位置识别的性能。

结论

在本文中，我们构建了带有疾病位置标签的舌头图像数据集，以挖掘舌头图像与疾病位置之间的关系。提出了一种新的全通道区域注意力网络来对局部细节舌头特征进行建模并提高建模效率。

意义

深度学习在舌象疾病定位识别中的应用和提出的创新模型对其他辅助诊断任务具有指导意义。所提出的模型提供了一个对详细舌头特征进行有效建模的例子，对其他辅助诊断应用具有重要的指导意义。