Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University, Hefei, China; School of Computer Science and Technology, Anhui University, Hefei, China.
Comput Biol Med. 2023 May;157:106790. doi: 10.1016/j.compbiomed.2023.106790. Epub 2023 Mar 15.
Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI scans are highly associated with AD. The degree of atrophy in patients' brain tissues and the distribution of lesion areas differ among patients. Therefore, a key challenge in sMRI-based AD diagnosis is identifying discriminating atrophy features. Hence, we propose a multiplane and multiscale feature-level fusion attention (MPS-FFA) model. The model has three components, (1) A feature encoder uses a multiscale feature extractor with hybrid attention layers to simultaneously capture and fuse multiple pathological features in the sagittal, coronal, and axial planes. (2) A global attention classifier combines clinical scores and two global attention layers to evaluate the feature impact scores and balance the relative contributions of different feature blocks. (3) A feature similarity discriminator minimizes the feature similarities among heterogeneous labels to enhance the ability of the network to discriminate atrophy features. The MPS-FFA model provides improved interpretability for identifying discriminating features using feature visualization. The experimental results on the baseline sMRI scans from two databases confirm the effectiveness (e.g., accuracy and generalizability) of our method in locating pathological locations. The source code is available at https://github.com/LiuFei-AHU/MPSFFA.
结构磁共振成像（sMRI）是一种广泛应用于阿尔茨海默病（AD）诊断的流行技术。然而，sMRI 扫描中只有少数结构萎缩区域与 AD 高度相关。患者脑组织的萎缩程度和病变区域的分布在患者之间存在差异。因此，基于 sMRI 的 AD 诊断的一个关键挑战是识别有区别的萎缩特征。为此，我们提出了一种多平面和多尺度特征级融合注意力（MPS-FFA）模型。该模型有三个组成部分，（1）特征编码器使用多尺度特征提取器和混合注意力层，同时捕获和融合矢状面、冠状面和轴面的多个病理特征。（2）全局注意力分类器结合临床评分和两个全局注意力层，评估特征影响分数并平衡不同特征块的相对贡献。（3）特征相似性鉴别器最小化异质标签之间的特征相似性，以增强网络区分萎缩特征的能力。MPS-FFA 模型通过特征可视化提供了对识别区分特征的可解释性的提高。来自两个数据库的基线 sMRI 扫描的实验结果证实了我们的方法在定位病理位置方面的有效性（例如，准确性和泛化能力）。源代码可在 https://github.com/LiuFei-AHU/MPSFFA 获得。
