医学图像模态分类与检索

G. Csurka, S. Clinchant, Guillaume Jacquet
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引用次数: 27

摘要

本文的目的是探讨不同的医学图像模式和检索策略。首先,我们分析了当前最先进的图像表示(视觉词袋和Fisher向量)在用于医学模态分类时的表现。然后将这些表示集成到一个基于内容的图像检索系统中,并在一个医学图像检索任务上进行了测试。最后,在这两种情况下,我们都探讨了将视觉信息与文本信息结合起来如何提高性能。为了展示不同系统的性能,我们将我们的方法与参加最新ImageClef挑战赛医疗任务的系统进行了比较[16]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Medical image modality classification and retrieval
The aim of this paper is to explore different medical image modality and retrieval strategies. First, we analyze how current state-of-the art image representations (bags of visual words and Fisher Vectors) perform when we use them for medical modality classification. Then we integrated these representations in a content based image retrieval system and tested on a medical image retrieval task. Finally, in both cases, we explored how the performance can be improved if we combine visual with textual information. To show the performance of different systems we compared our approaches to the systems participated at the Medical Task of the latest ImageClef Challenge [16].
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