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超声图像的理解与标注文本生成

Understanding and Generating Ultrasound Image Description

  • 摘要: 为了更方便更快速地理解超声图像的内容,本文提出了一种由粗到精的超声图像主题生成的集成模型,该模型可以自动生成由相关的n-gram组成的注释文本来描述超声图像中的疾病信息。首先通过粗分类模型检测超声图像中的器官;然后根据器官标签通过相应的细分类模型对超声图像进行编码;最后将编码向量输入到语言生成模型中,就能自动生成注释文本来描述超声图像中的疾病信息。实验结果表明,编码模型能够在超声图像识别中获得较高的准确率,语言生成模型可以自动生成高质量的注释文本。在实际应用中,该模型可以帮助患者和医生更好地理解超声图像的内容。

     

    Abstract: To understand the content of ultrasound images more conveniently and more quickly, in this paper, we propose a coarse-to-fine ultrasound image captioning ensemble model, which can automatically generate the annotation text that is composed of relevant n-grams to describe the disease information in the ultrasound images. First, the organs in the ultrasound images are detected by the coarse classification model. Second, the ultrasound images are encoded by the corresponding fine-grained classification model according to the organ labels. Finally, we input the encoding vectors to the language generation model, and the language generation model generates automatically annotation text to describe the disease information in the ultrasound images. In our experiments, the encoding model can obtain the high accuracy rate in the ultrasound image recognition. And the language generation model can automatically generate high-quality annotation text. In practical applications, the coarse-to-fine ultrasound image captioning ensemble model can help patients and doctors obtain the well understanding of the contents of ultrasound images.

     

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