National Institutes of Health, Bethesda, MD 20892, USA.
IEEE Trans Med Imaging. 2012 May;31(5):1141-53. doi: 10.1109/TMI.2012.2187304.
In this paper, we present development and testing results for a novel colonic polyp classification method for use as part of a computed tomographic colonography (CTC) computer-aided detection (CAD) system. Inspired by the interpretative methodology of radiologists using 3-D fly-through mode in CTC reading, we have developed an algorithm which utilizes sequences of images (referred to here as videos) for classification of CAD marks. For each CAD mark, we created a video composed of a series of intraluminal, volume-rendered images visualizing the detection from multiple viewpoints. We then framed the video classification question as a multiple-instance learning (MIL) problem. Since a positive (negative) bag may contain negative (positive) instances, which in our case depends on the viewing angles and camera distance to the target, we developed a novel MIL paradigm to accommodate this class of problems. We solved the new MIL problem by maximizing a L2-norm soft margin using semidefinite programming, which can optimize relevant parameters automatically. We tested our method by analyzing a CTC data set obtained from 50 patients from three medical centers. Our proposed method showed significantly better performance compared with several traditional MIL methods.
本文提出了一种新型的结肠息肉分类方法，用于作为计算机断层结肠成像（CTC）计算机辅助检测（CAD）系统的一部分。受放射科医生在 CTC 阅读中使用 3D 飞行模式的解释方法的启发，我们开发了一种算法，该算法利用图像序列（这里称为视频）对 CAD 标记进行分类。对于每个 CAD 标记，我们创建了一个由一系列内腔容积渲染图像组成的视频，这些图像从多个角度可视化检测。然后，我们将视频分类问题表述为一个多实例学习（MIL）问题。由于正（负）袋可能包含负（正）实例，在我们的情况下，这取决于目标的视角和相机距离，因此我们开发了一种新的 MIL 范例来适应这类问题。我们通过使用半定规划最大化 L2 范数软间隔来解决新的 MIL 问题，该方法可以自动优化相关参数。我们通过分析来自三个医疗中心的 50 名患者的 CTC 数据集来测试我们的方法。与几种传统的 MIL 方法相比，我们提出的方法表现出了显著更好的性能。
