A SEQUENCE AGNOSTIC MULTIMODAL PRE-PROCESSING FOR CLOGGED BLOOD VESSEL DETECTION IN ALZHEIMER'S DIAGNOSIS

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 pre-processing 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 bidirection dataflow. Experimental results on The Clog Loss dataset(1) show that our proposed method consistently outperforms the state-of-the-art pre-processing methods in stalled and non-stalled vessel classification.