Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis

• URL: http://arxiv.org/abs/2306.05297v1
• Date: Thu, 8 Jun 2023 15:39:27 GMT
• Title: Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis
• Authors: Gongshu Wang, Ning Jiang, Yunxiao Ma, Tiantian Liu, Duanduan Chen, Jinglong Wu, Guoqi Li, Dong Liang, Tianyi Yan
• Abstract summary: We propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain. CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases.
• Score: 12.172262618438173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous approaches focused on local shapes and textures in sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have a poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, better aggregates features, is easier to optimize and is more robust to noise, which explains its superiority in theory. Our source code will be released soon.

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