超越KernelBoost
Beyond KernelBoost
摘要：
在本技术报告中，我们提出了一组关于KernelBoost分类器的改进[Becker等人，MICCAI 2013]。我们从一个受Auto-Context启发的方案开始，但这适用于缺乏大型训练集的情况，这可能会带来过度拟合的问题。其目的是捕捉相邻图像像素之间的相互作用，以更好地规则化分割区域的边界。如在Auto-Context [Tu et al.，PAMI 2009]，分割过程是迭代的，并且在每次迭代时，结合图像本身考虑先前迭代的分割结果。然而，与[Tu等人，PAMI 2009]，我们组织我们的递归，使分类器可以逐步集中在难以分类的位置。这使我们能够利用决策树范式的力量，同时避免过度拟合。在这种架构的上下文中，KernelBoost代表了一个强大的构建块，因为它能够在来自先前迭代的分数图上学习。我们首先介绍了两种重要的机制来增强KernelBoost分类器，即基于相应的地面实况的出现对阳性样本进行池化和聚类。这些操作显着有助于提高生物医学图像，其中纹理起着重要作用，在不同的图像成分的识别系统的有效性。然后，我们提出了一些其他技术，可以很容易地集成到KernelBoost框架中，以进一步提高最终分割的准确性。我们在不同的医学图像数据集上展示了广泛的结果，包括一些多标签任务，在这些任务上，我们的方法表现优于最先进的方法。结果分割显示高精度，整洁的轮廓，并减少噪音。
英文摘要：
In this Technical Report we propose a set of improvements with respect to the KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with a scheme inspired by Auto-Context, but that is suitable in situations where the lack of large training sets poses a potential problem of overfitting. The aim is to capture the interactions between neighboring image pixels to better regularize the boundaries of segmented regions. As in Auto-Context [Tu et al., PAMI 2009] the segmentation process is iterative and, at each iteration, the segmentation results for the previous iterations are taken into account in conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009], we organize our recursion so that the classifiers can progressively focus on difficult-to-classify locations. This lets us exploit the power of the decision-tree paradigm while avoiding over-fitting. In the context of this architecture, KernelBoost represents a powerful building block due to its ability to learn on the score maps coming from previous iterations. We first introduce two important mechanisms to empower the KernelBoost classifier, namely pooling and the clustering of positive samples based on the appearance of the corresponding ground-truth. These operations significantly contribute to increase the effectiveness of the system on biomedical images, where texture plays a major role in the recognition of the different image components. We then present some other techniques that can be easily integrated in the KernelBoost framework to further improve the accuracy of the final segmentation. We show extensive results on different medical image datasets, including some multi-label tasks, on which our method is shown to outperform state-of-the-art approaches. The resulting segmentations display high accuracy, neat contours, and reduced noise.
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。
