A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel
Detection in Alzheimer's Diagnosis
- URL: http://arxiv.org/abs/2211.03109v1
- Date: Sun, 6 Nov 2022 13:22:05 GMT
- Title: A Sequence Agnostic Multimodal Preprocessing for Clogged Blood Vessel
Detection in Alzheimer's Diagnosis
- Authors: Partho Ghosh, Md. Abrar Istiak, Mir Sayeed Mohammad, Swapnil Saha,
Uday Kamal
- Abstract summary: Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis.
In this study, we propose several preprocessing schemes to improve the performance of machine learning methods.
- Score: 1.394948342529531
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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