Tooth-marked tongue or crenated tongue can provide valuable diagnostic information for traditional Chinese Medicine doctors. However, tooth-marked tongue recognition is challenging. The characteristics of different tongues are multiform and have a great amount of variations, such as different colors, different shapes, and different types of teeth marks. The regions of teeth mark only appear along the lateral borders. Most existing methods make use of concave regions information to classify the tooth-marked tongue which leads to inconstant performance when the region of teeth mark is not concave. In this paper, we try to solve these problems by proposing a three-stage approach which first makes use of concavity information to propose the suspected regions, then use a convolutional neural network to extract deep features and at last use a multiple-instance classifier to make the final decision. Experimental results demonstrate the effectiveness of the proposed method.

中文翻译：

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使用多实例学习和 CNN 特征的齿痕舌头识别


齿痕舌或锯齿舌可为中医提供有价值的诊断信息。然而，齿痕舌头识别具有挑战性。不同舌头的特征是多种多样的，变化也很大，如不同的颜色、不同的形状、不同类型的齿痕等。齿痕区域仅出现在侧缘。现有的方法大多利用凹入区域信息对齿痕舌头进行分类，当齿痕区域不凹入时，导致性能不稳定。在本文中，我们试图通过提出一种三阶段方法来解决这些问题，首先利用凹性信息来提出可疑区域，然后使用卷积神经网络来提取深层特征，最后使用多实例分类器来做出最终决定。实验结果证明了该方法的有效性。