sMRI-PatchNet: a novel efficient explainable patch-based deep learning network for Alzheimer’s Disease diagnosis with structural MRI

Zhang, Xin ORCID: https://orcid.org/0000-0001-7844-593X, Han, Liangxiu ORCID: https://orcid.org/0000-0003-2491-7473, Han, Lianghao ORCID: https://orcid.org/0000-0003-2491-7473, Chen, Haoming, Dancey, Darren ORCID: https://orcid.org/0000-0001-7251-8958 and Zhang, Daoqiang (2023) sMRI-PatchNet: a novel efficient explainable patch-based deep learning network for Alzheimer’s Disease diagnosis with structural MRI. IEEE Access, 11. pp. 108603-108616. ISSN 2169-3536

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Abstract

Structural magnetic resonance imaging (sMRI) can identify subtle brain changes due to its high contrast for soft tissues and high spatial resolution. It has been widely used in diagnosing neurological brain diseases, such as Alzheimer’s disease (AD). However, the size of 3D high-resolution data poses a significant challenge for data analysis and processing. Since only a few areas of the brain show structural changes highly associated with AD, the patch-based methods dividing the whole data into several regular patches have shown promising for more efficient image analysis. The major challenges of the patch-based methods include identifying the discriminative patches, combining features from the discrete discriminative patches, and designing appropriate classifiers. This work proposes a novel efficient patch-based deep learning network (sMRI-PatchNet) with explainable patch localisation and selection for AD diagnosis. Specifically, it consists of two primary components: 1) A fast and efficient explainable patch selection method for determining the most discriminative patches; and 2) A novel patch-based network for extracting deep features and AD classification with position embeddings to retain position information, capable of capturing the global and local information of inter- and intra-patches. This method has been applied for the AD classification and the prediction of the transitional state moderate cognitive impairment (MCI) conversion with real datasets. The experimental evaluation shows that the proposed method can identify discriminative pathological locations effectively with a significant reduction on patch numbers used, providing better performance in terms of accuracy, computing performance, and generalizability, in contrast to the state-of-the-art methods.