Singh Vikas, Mukherjee Lopamudra, Chung Moo K
Biostatistics and Medical Informatics, University of Wisconsin-Madison, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):999-1007. doi: 10.1007/978-3-540-85988-8_119.
We study the problem of classifying an autistic group from controls using structural image data alone, a task that requires a clinical interview with a psychologist. Because of the highly convoluted brain surface topology, feature extraction poses the first obstacle. A clinically relevant measure called the cortical thickness has shown promise but yields a rather challenging learning problem--where the dimensionality of the distribution is extremely large and the training set is small. By observing that each point on the brain cortical surface may be treated as a "hypothesis", we propose a new algorithm for LPBoosting (with truncated neighborhoods) for this problem. In addition to learning a high quality classifier, our model incorporates topological priors into the classification framework directly - that two neighboring points on the cortical surface (hypothesis pairs) must have similar discriminative qualities. As a result, we obtain not just a label {+1, -1} for test items, but also an indication of the "discriminative regions" on the cortical surface. We discuss the formulation and present interesting experimental results.
我们仅使用结构图像数据研究从对照组中对自闭症群体进行分类的问题，这项任务需要与心理学家进行临床访谈。由于大脑表面拓扑结构高度复杂，特征提取成为首要障碍。一种名为皮质厚度的临床相关测量方法已显示出前景，但产生了一个颇具挑战性的学习问题——分布的维度极大且训练集很小。通过观察到大脑皮质表面的每个点都可被视为一个“假设”，我们针对此问题提出了一种新的（具有截断邻域的）LPBoosting算法。除了学习高质量的分类器外，我们的模型还将拓扑先验直接纳入分类框架——皮质表面上的两个相邻点（假设对）必须具有相似的判别质量。结果，我们不仅为测试项目获得一个标签{+1, -1}，还能得到皮质表面“判别区域”的指示。我们讨论了该公式并展示了有趣的实验结果。
