Deep Forest With Multi-Channel Message Passing and Neighborhood Aggregation Mechanisms for Brain Network Classification.

Abstract

As a novel deep learning method, deep forest has achieved excellent classification performance on many small-scale datasets, thus providing a new opportunity to accurately classify brain networks (BNs) on limited fMRI data. Though there are a few explorations about classifying BNs using deep forest, they only adopt sliding windows to extract adjacent features of BNs and fail to use prior knowledge to strengthen the features more relevant to brain diseases. In this paper, we propose a deep forest framework with multi-channel message passing and neighborhood aggregation mechanisms (DF-MCMPNA) to extract and aggregate long-range multi-channel topological features. Firstly, we use the three intrinsic connectivity networks (ICNs) and the whole-brain to form four feature extraction channels. Secondly, we present a multi-channel message passing mechanism and a channel-shared neighborhood aggregation mechanism to recursively extract long-range multi-channel topological features, where the first mechanism can learn local topological features in each channel and the second mechanism can fuse multi-channel topological features. Finally, the extracted features are fed into the casForst to perform further feature learning and classification. Experimental results on ABIDE I, ABIDE II, and ADHD-200 datasets show that the DF-MCMPNA outperforms several state-of-the-art methods on classification performance and accurately identifies abnormal brain regions.