舌裂可视化与深度学习

Wen-Hsien Chang, H. Chu, Hen-Hong Chang
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引用次数: 4

摘要

舌诊是中医中一项独特的实践,可以用来推断一个人的健康状况。然而,不同的中医可能对同一种舌头给出不同的解释。如果可以基于大量医生解读的舌头图像开发人工智能模型,将获得更客观的判断。人工智能中的深度学习在图像识别方面表现优异,无需图像处理专家,通过深度学习即可自动完成特征提取。本研究试图通过大量的舌头图像,特别是舌裂图像,开发一个深度学习模型。我们还使用梯度加权类激活映射(gradcam)可视化裂缝区域。因此,该模型不仅尝试检测舌裂,而且对舌裂区域进行了定位。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tongue Fissure Visualization with Deep Learning
Tongue diagnosis is a unique practice in traditional Chinese medicine(TCM), which can be used to infer the health condition of a person. However, different TCM doctors may give different interpretations on the same tongue. If an artificial intelligence model can be developed based on a large number of doctor-interpreted tongue images, a more objective judgment will be obtained. Deep learning in artificial intelligence has excellent performance in image recognition, and feature extraction can be done automatically by deep learning without image processing experts. This study attempts to develop a deep learning model through a large number of tongue images, especially for tongue fissures. We also visualize the fissure regions with Gradient-weighted Class Activation Mapping(Grad-cam). Therefore, the model not only try to detect tongue fissures but also localize tongue fissure regions.
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