X射线散射图像多属性学习模型的交互式可视化研究
Interactive Visual Study of Multiple Attributes Learning Model of X-Ray Scattering Images
摘要：
现有的用于深度学习的交互式可视化工具大多应用于训练、调试和细化处理自然图像的神经网络模型。然而，视觉分析工具缺乏的具体应用的X射线图像分类与多个结构属性。在本文中，我们提出了一个交互式系统领域的科学家可视化研究的多属性学习模型应用于X射线散射图像。它允许领域科学家在嵌入式空间中交互式地探索这种重要类型的科学图像，这些空间是在模型预测输出、实际标签和神经网络的发现特征空间上定义的。用户可以灵活地选择实例图像，它们的集群，并根据指定的属性视觉表示进行比较。探索是由属性之间的相互关系，这往往影响学习的准确性和有效性的模型性能的表现形式。因此，该系统支持领域科学家改进训练数据集和模型，找到可疑的属性标签，并识别离群图像或虚假数据集群。案例研究和科学家的反馈证明了其功能和实用性。
英文摘要：
Existing interactive visualization tools for deep learning are mostly applied to the training, debugging, and refinement of neural network models working on natural images. However, visual analytics tools are lacking for the specific application of x-ray image classification with multiple structural attributes. In this paper, we present an interactive system for domain scientists to visually study the multiple attributes learning models applied to x-ray scattering images. It allows domain scientists to interactively explore this important type of scientific images in embedded spaces that are defined on the model prediction output, the actual labels, and the discovered feature space of neural networks. Users are allowed to flexibly select instance images, their clusters, and compare them regarding the specified visual representation of attributes. The exploration is guided by the manifestation of model performance related to mutual relationships among attributes, which often affect the learning accuracy and effectiveness. The system thus supports domain scientists to improve the training dataset and model, find questionable attributes labels, and identify outlier images or spurious data clusters. Case studies and scientists feedback demonstrate its functionalities and usefulness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
