间接免疫荧光图像分类的分位数表示
Quantile Representation for Indirect Immunofluorescence Image Classification
摘要：
在自身免疫性疾病的诊断中，一个重要的任务是对含有几个HEp-2细胞的载玻片的图像进行分类。来自一个载玻片的所有细胞共享相同的标签，并且通过独立地对来自一个载玻片的细胞进行分类，丢失了关于全局图像质量和强度的一些信息。然而，将一整张幻灯片视为特征向量的集合（一个袋子），提出了如何处理这个袋子的问题。一种简单且令人惊讶的有效方法是通过每个特征的几个分位数值来总结特征向量。这表征了所有实例的完全分布，从而假设包中的所有实例都是信息性的。当每个袋子包含许多特征向量时，这种表示特别有用，这是免疫荧光图像分类的情况。对间接免疫荧光图像的分类实验表明了该方法的有效性。
英文摘要：
In the diagnosis of autoimmune diseases, an important task is to classify images of slides containing several HEp-2 cells. All cells from one slide share the same label, and by classifying cells from one slide independently, some information on the global image quality and intensity is lost. Considering one whole slide as a collection (a bag) of feature vectors, however, poses the problem of how to handle this bag. A simple, and surprisingly effective, approach is to summarize the bag of feature vectors by a few quantile values per feature. This characterizes the full distribution of all instances, thereby assuming that all instances in a bag are informative. This representation is particularly useful when each bag contains many feature vectors, which is the case in the classification of the immunofluorescence images. Experiments on the classification of indirect immunofluorescence images show the usefulness of this approach.
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。
