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System and method for multiple instance learning for computer aided detection

机译:用于计算机辅助检测的多实例学习的系统和方法

摘要

A method of training a classifier for computer aided detection of digitized medical image, includes providing a plurality of bags, each bag containing a plurality of feature samples of a single region-of-interest in a medical image, where each region-of-interest has been labeled as either malignant or healthy. The training uses candidates that are spatially adjacent to each other, modeled by a “bag”, rather than each candidate by itself. A classifier is trained on the plurality of bags of feature samples, subject to the constraint that at least one point in a convex hull of each bag, corresponding to a feature sample, is correctly classified according to the label of the associated region-of-interest, rather than a large set of discrete constraints where at least one instance in each bag has to be correctly classified.
机译:一种训练用于计算机辅助检测数字化医学图像的分类器的方法,包括提供多个袋,每个袋包含医学图像中单个感兴趣区域的多个特征样本,其中每个感兴趣区域被标记为恶性或健康。训练使用的是在空间上彼此相邻的候选人(以“包”为模型),而不是每个候选人本身。在多个袋子的特征样本上训练分类器,但要遵循以下约束:根据相关区域的标签正确分类每个袋子的凸包中至少一个与特征样本相对应的点。而不是一大堆离散的约束条件,在这种约束条件下,每个袋子中的至少一个实例必须正确分类。

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