Seshamani S, Kumar R, Dassopoulos T, Mullin G, Hager G
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):454-62. doi: 10.1007/978-3-642-15745-5_56.
The current procedure for diagnosis of Crohn's disease (CD) from Capsule Endoscopy is a tedious manual process which requires the clinician to visually inspect large video sequences for matching and categorization of diseased areas (lesions). Automated methods for matching and classification can help improve this process by reducing diagnosis time and improving consistency of categorization. In this paper, we propose a novel SVM-based similarity learning method for distinguishing between correct and incorrect matches in Capsule Endoscopy (CE). We also show that this can be used in conjunction with a voting scheme to categorize lesion images. Results show that our methods outperform standard classifiers in discriminating similar from dissimilar lesion images, as well as in lesion categorization. We also show that our methods drastically reduce the complexity (training time) by requiring only one half of the data for training, without compromising the accuracy of the classifier.
目前通过胶囊内镜诊断克罗恩病（CD）的流程是一个繁琐的手动过程，需要临床医生目视检查大量视频序列，以便对病变区域（病灶）进行匹配和分类。用于匹配和分类的自动化方法有助于通过减少诊断时间和提高分类的一致性来改进这一过程。在本文中，我们提出了一种新颖的基于支持向量机（SVM）的相似性学习方法，用于区分胶囊内镜（CE）中的正确匹配和错误匹配。我们还表明，这可以与投票方案结合使用，对病变图像进行分类。结果表明，我们的方法在区分相似和不相似病变图像以及病变分类方面优于标准分类器。我们还表明，我们的方法通过仅需要一半的数据进行训练，在不影响分类器准确性的情况下，大幅降低了复杂度（训练时间）。
