论文标题
在阿尔茨海默氏症诊断中检测堵塞血管检测的序列不可知论的多模式预处理
A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel Detection in Alzheimer's Diagnosis
论文作者
论文摘要
成功识别血管阻塞是阿尔茨海默氏病诊断的关键步骤。可以使用机器学习方法从空间和时间深度可变的两光激发显微镜（TPEF）图像中识别这些块。在这项研究中，我们提出了几种预处理方案，以提高这些方法的性能。我们的方法包括从图像方式及其功能空间融合中提取的3D点云数据提取，以利用不同方式固有的补充信息。我们还通过利用双向数据流来强制实施学习的表示形式是序列阶的不变性。堵塞丢失数据集的实验结果表明，我们提出的方法始终优于停滞和非固定血管分类中最新的预处理方法。
Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. These blocks can be identified from the spatial and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the brain blood vessels using machine learning methods. In this study, we propose several preprocessing schemes to improve the performance of these methods. Our method includes 3D-point cloud data extraction from image modality and their feature-space fusion to leverage complementary information inherent in different modalities. We also enforce the learned representation to be sequence-order invariant by utilizing bi-direction dataflow. Experimental results on The Clog Loss dataset show that our proposed method consistently outperforms the state-of-the-art preprocessing methods in stalled and non-stalled vessel classification.
