sMRI-PatchNet: A novel explainable patch-based deep learning network for
Alzheimer's disease diagnosis and discriminative atrophy localisation with
Structural MRI
- URL: http://arxiv.org/abs/2302.08967v2
- Date: Mon, 20 Feb 2023 02:03:55 GMT
- Title: sMRI-PatchNet: A novel explainable patch-based deep learning network for
Alzheimer's disease diagnosis and discriminative atrophy localisation with
Structural MRI
- Authors: Xin Zhang, Liangxiu Han, Lianghao Han, Haoming Chen, Darren Dancey,
Daoqiang Zhang
- Abstract summary: The size of 3D high-resolution data poses a significant challenge for data analysis and processing.
The patch-based methods dividing the whole image data into several small regular patches have shown promising for more efficient sMRI-based image analysis.
This work proposes a novel patch-based deep learning network (sMRI-PatchNet) with explainable patch localisation and selection for Alzheimer disease diagnosis using sMRI.
- Score: 18.234996137020406
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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 image data into several small regular patches have
shown promising for more efficient sMRI-based image analysis. The major
challenges of the patch-based methods on sMRI include identifying the
discriminative patches, combining features from the discrete discriminative
patches, and designing appropriate classifiers. This work proposes a novel
patch-based deep learning network (sMRI-PatchNet) with explainable patch
localisation and selection for AD diagnosis using sMRI. Specifically, it
consists of two primary components: 1) A fast and efficient explainable patch
selection mechanism for determining the most discriminative patches based on
computing the SHapley Additive exPlanations (SHAP) contribution to a transfer
learning model for AD diagnosis on massive medical data; and 2) A novel
patch-based network for extracting deep features and AD classfication from the
selected patches 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.
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