An Image-Enhanced Topic Modeling Method for Neuroimaging Literature
An Image-Enhanced Topic Modeling Method for Neuroimaging Literature
复制标题
DOI：
10.1007/978-3-030-86993-9_28
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Lianfang Ma;Jianhui Chen;Ning Zhong
中科院分区：
文献类型：
--
作者：
Lianfang Ma;Jianhui Chen;Ning Zhong
基于神经影像文献的主题建模是聚合国内外研究成果、解码大脑认知机制、脑与精神疾病诊治、人工智能研究等的重要途径。然而，现有的神经影像文献挖掘仅关注文本，忽视了包含大量主题信息的脑图像。遵循图像与文本结合的写作和阅读习惯，我们在本文中提出了一种图像增强的LDA（潜在狄利克雷分配），它从神经影像图像和全文中提取文献主题。将功能磁共振成像大脑区域激活图像的主题与全文的主题相结合，可以更准确地对神经影像文献进行建模。一方面，可以从激活的大脑图像及其描述中针对性地提取与大脑认知机制相关的主题。另一方面，来自激活的大脑图像的主题可以与全文的主题相结合，以更准确地对神经影像文献进行建模。基于实际数据的实验初步证明了所提方法的有效性。
Topic modeling based on neuroimaging literature is an important approach to aggregate world-wide research findings for decoding brain cognitive mechanism, as well as diagnosis and treatment of brain and mental diseases, artificial intelligence researches, etc. However, existing neuroimaging literature mining only focused on texts and neglects brain images which contain a large amount of topic information. Following the writing and reading habits combining images with texts, we present in this paper an image-enhanced LDA (Latent Dirichlet Allocation), which extracts literature topics from both neuroimaging images and full texts. Combining topics from fMRI brain regions activation images with topics from full texts to model neuroimaging literatures more accurately. On the one hand, topics related brain cognitive mechanism can be pertinently extracted from activated brain images and their descriptions. On the other hand, topics from activated brain images can be integrated with topics from full text to model neuroimaging literature more accurately. The experiments based on actual data has preliminarily proved effectiveness of proposed method.