Dinh Thien Anh, Silander Tomi, Lim C C Tchoyoson, Leong Tze-Yun
National University of Singapore, Singapore.
AMIA Annu Symp Proc. 2011;2011:312-21. Epub 2011 Oct 22.
This paper proposes a generative model approach to automatically annotate medical images to improve the efficiency and effectiveness of image retrieval systems for teaching, research, and diagnosis. The generative model captures the probabilistic relationships among relevant classification tags, tentative lesion patterns, and selected input features. Operating on the imperfect segmentation results of input images, the probabilistic framework can effectively handle the inherent uncertainties in the images and insufficient information in the training data. Preliminary assessment in the ischemic stroke subtype classification shows that the proposed system is capable of generating the relevant tags for ischemic stroke brain images. The main benefit of this approach is its scalability; the method can be applied in large image databases as it requires only minimal manual labeling of the training data and does not demand high-precision segmentation of the images.
本文提出了一种生成模型方法，用于自动标注医学图像，以提高用于教学、研究和诊断的图像检索系统的效率和有效性。生成模型捕捉相关分类标签、暂定病变模式和选定输入特征之间的概率关系。该概率框架基于输入图像的不完美分割结果进行操作，能够有效处理图像中固有的不确定性以及训练数据中信息不足的问题。对缺血性中风亚型分类的初步评估表明，所提出的系统能够为缺血性中风脑图像生成相关标签。这种方法的主要优点是其可扩展性；该方法可应用于大型图像数据库，因为它只需要对训练数据进行最少的人工标注，并且不要求对图像进行高精度分割。
