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为了有效地提升对阿尔茨海默病(AD)的磁共振成像(MRI)图像分类准确率，提出一种LC(Layer-Cut)-ViT方法。该方法通过引入Vision Transformer(ViT)的自注意力机制对MRI图像进行层切分，使模型能更好地理解图像的全局信息，同时突出切片间的特征关系。此外，通过配准、颅骨分离算法提取MRI图像的脑部组织部分，进一步提升模型的性能。实验结果显示，所提方法对阿尔茨海默病的MRI图像具有较好的分类能力。
Abstract：To effectively improve the classification accuracy of magnetic resonance imaging(MRI) for Alzheimer's disease(AD), we propose an LC(Layer-Cut)-ViT method in this paper. This method introduces the self-attention mechanism of the Vision Transformer(ViT) and performs layer-wise segmentation on the MRI images, to enable the model to better understand the global information of the images while emphasizing the inter-slice feature relationships. Additionally, the extraction of brain tissue from the MRI images is further enhanced by employing registration and skull-stripping algorithms, which results in improved performance of the model. Experimental results demonstrate that the proposed method exhibits good classification ability for MRI images of Alzheimer's disease.
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基本信息:
中图分类号:TP391.41;R749.16
引用信息:
[1]许曙博,郑英豪,秦方博,等.基于Vision Transformer的阿尔茨海默病分类研究[J].微型电脑应用,2024,40(08):4-7.
基金信息:
2023年广东省科技创新战略专项资金项目(pdjh2023a0775)
2024-08-20
2024-08-20