Nonlinear Dimensionality Reduction of Brain MRI Data for Classification Applications

分类，或将图像分类为有意义的组，是脑图像分析中的重要应用。简化是许多分类算法的关键组成部分，对于降低处理脑图像的数学方法的复杂性至关重要。日常使用中的降维的一个简单示例是将3D地理投影到2D地图上。不同的投影方法保留了地理的不同属性，就像不同的降维方法保留了大脑图像的不同属性一样。最近，机器学习领域的突破性发现重新点燃了人们对一种称为深度信念网（DBN）的神经网络的浓厚兴趣，其已经被证明在通过减少图像的维度以形成简化的虚拟景观来自动学习大组小图像中的相似性模式方面非常有效，被群山隔开。事实证明，这种方法在识别人脸和手写数字方面比几乎所有其他方法都更准确。与以前用于DBN实验的数据相比，大脑图像具有更高的维度，并且具有更复杂的形状和强度变化。我们的长期研究目标是开发新的DBN方法，可用于形成大脑图像组的简化景观，使山谷代表可用于分类的有意义的相似性。使用一个非常大的数据库的磁共振图像（MRI）的多发性硬化症患者，我们将开发新的方法，使DBN与大型，复杂的图像。我们将研究DBN能够从脑图像组中提取哪些类型的相似性，并确定这些相似性是否可能用于将脑图像分类为各种类型的类别，这些类别的范围从视觉上显而易见的到非图像衍生的，例如临床结果。如果成功，这项工作将使神经成像研究人员受益匪浅，使他们能够更有效地利用他们的数据。

Classification, or the categorization of images into meaningful groups, is an important application in brain image analysis. Dimensionality reduction is a key component of many classification algorithms and is essential for reducing the complexity of mathematical methods for working with brain images. A simple example of dimensionality reduction in everyday use is the projection of 3D geography onto 2D maps. Different projection methods preserve different properties of the geography, just like different dimensionality reduction methods preserve different properties of brain images. Recently, groundbreaking discoveries in the field of machine learning have rekindled a strong interest in a type of neural network called the deep belief net (DBN), which has been shown to be very effective at automatically learning patterns of similarity in large groups of small images by reducing the dimensionality of the images to form a simplified virtual landscape in which similar images occupy the same valleys and dissimilar images are separated by hills. This approach has been shown to be more accurate for identifying faces and handwritten digits than practically all other methods. In comparison to the previous data used for DBN experiments, brain images are of much higher dimension and have much more complex shape and intensity variations. Our long-term research goal is to develop new DBN methods that can be used to form simplified landscapes of groups of brain images such that the valleys represent meaningful similarities that can be used for classification. Using a very large database of magnetic resonance images (MRIs) of patients with multiple sclerosis, we will develop new methods for making DBNs work with large, complex images. We will investigate what types of similarities DBNs are capable of extracting from groups of brain images, and determine whether those similarities are potentially useful for classifying brain images into various types of categories that will range from the visually obvious to ones that are not image-derived, such as clinical outcomes. If successful, this work will greatly benefit researchers in neuroimaging by allowing them to make much more effective use of their data.