Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional learning strategies. However, a key challenge with deep learning models is understanding just what image features the network is using, which can in turn be used to define the biomarkers. Current methods extract biomarkers, i.e., important features, by looking at how the prediction changes if "ignoring" one feature at a time. In this work, we go beyond looking at only individual features by using Shapley value explanation (SVE) from cooperative game theory. Cooperative game theory is advantageous here because it directly considers the interaction between features and can be applied to any machine learning method, making it a novel, more accurate way of determining instance-wise biomarker importance from deep learning models. A barrier to using SVE is its computational complexity: $2^N$ given $N$ features. We explicitly reduce the complexity of SVE computation by two approaches based on the underlying graph structure of the input data: 1) only consider the centralized coalition of each feature; 2) a hierarchical pipeline which first clusters features into small communities, then applies SVE in each community. Monte Carlo approximation can be used for large permutation sets. We first validate our methods on the MNIST dataset and compare to human perception. Next, to insure plausibility of our biomarker results, we train a Random Forest (RF) to classify ASD/control subjects from fMRI and compare SVE results to standard RF-based feature importance. Finally, we show initial results on ranked fMRI biomarkers using SVE on a deep learning classifier for the ASD/control dataset.
翻译:发现自闭症谱系障碍(ASD)的成像生物标志,对于帮助解释 ASD 和预测或监测治疗结果至关重要。 为此,最近使用了深学习分类方法,从功能磁共振成像(fMRI)中识别ASD,其精度高于传统的学习战略。然而,深学习模型的关键挑战在于理解网络正在使用的成像特征,这反过来可以用来定义生物标志。当前的方法可以提取生物标志,即重要特征,通过观察如果“不见”一个特性时,预测会如何变化。在这项工作中,我们不仅仅通过使用合作游戏理论中的“SVE”价值解释(SVE)来查看单个特性。在这里,合作游戏理论是有利的,因为它直接考虑到各特性之间的相互作用,并可以应用于任何机器学习方法,因此它是一种新颖的、更准确的方法,用来确定深海学习模型中的比比喻生物标志的重要性。 使用SVE的初始障碍是其计算复杂性:我们给了美元。我们只用美元来降低SVE的初始复杂性,我们用两种方法来计算一个基础的SVEVeal 数据结构结构结构结构结构结构结构结构的每个模型结构结构中,然后应用SRBSLSLSL数据。