Electronics and Computing Institute, Universidad Tecnológica de la Mixteca, 69000, Huajuapan, Oaxaca, México.
Int J Neural Syst. 2011 Feb;21(1):17-29. doi: 10.1142/S0129065711002626.
Medical diagnosis can often be understood as a classification problem. In oncology, this typically involves differentiating between tumour types and grades, or some type of discrete outcome prediction. From the viewpoint of computer-based medical decision support, this classification requires the availability of accurate diagnoses of past cases as training target examples. The availability of such labeled databases is scarce in most areas of oncology, and especially so in neuro-oncology. In such context, semi-supervised learning oriented towards classification can be a sensible data modeling choice. In this study, semi-supervised variants of Generative Topographic Mapping, a model of the manifold learning family, are applied to two neuro-oncology problems: the diagnostic discrimination between different brain tumour pathologies, and the prediction of outcomes for a specific type of aggressive brain tumours. Their performance compared favorably with those of the alternative Laplacian Eigenmaps and Semi-Supervised SVM for Manifold Learning models in most of the experiments.
医学诊断通常可以被理解为一个分类问题。在肿瘤学中，这通常涉及区分肿瘤类型和分级，或某种离散的结果预测。从基于计算机的医学决策支持的角度来看，这种分类需要有过去病例的准确诊断作为训练目标示例。在大多数肿瘤学领域，特别是神经肿瘤学领域，这种标记数据库的可用性非常有限。在这种情况下，面向分类的半监督学习可能是一种合理的数据建模选择。在这项研究中，生成拓扑映射的半监督变体（流形学习家族的一种模型）被应用于两个神经肿瘤学问题：不同脑肿瘤病理的诊断区分，以及特定类型侵袭性脑肿瘤的结果预测。在大多数实验中，它们的性能都优于替代的拉普拉斯特征映射和流形学习的半监督 SVM 模型。
