NIDC: General Task Backbone for Neuroimaging Analysis via Interpretable Deep Clustering

Clustering techniques offer strong interpretability. However, they have significant limitations in the deep learning area due to their difficulty in capturing complex data structures, such as spatial and contextual information. This issue is especially pronounced in neuroimaging research, where high-dimensional and complex data greatly restricts the feature representation capability of clustering models. Hence, we propose a Neuroimaging Deep Clustering (NIDC) backbone network. We convert 3D neuroimagings into point sets and design clustering-based paradigms for context feature aggregation, feature interaction, and feature dispatching to enable deep feature extraction from the point sets. To better capture spatial information, we propose brain spatial relative position encoding, which assists the clustering paradigm in better understanding the anatomical structure of brain tissue and the positional relationships between different regions. Additionally, we design a sample center loss function to encourage tighter clustering of labels or voxels/feature points of the same class in the feature space, aiming to suppress both inter-class and intra-class similarities. Meanwhile, NIDC preserves the interpretability of traditional clustering techniques, allowing it to uncover relationships between brain regions and trace the decision-making process of each voxel. NIDC achieves highly competitive performance across multiple datasets in various downstream tasks, emerging as a new and practical solution for neuroimaging analysis. Code is available athttps://github.com/IMCTGD/NIDC.