Abstract
Mild Cognitive Impairment (MCI) is the prodromal stage of Alzheimer’s disease (AD), with a high incident rate converting to AD. Hence, it is critical to identify MCI patients who will convert to AD patients for early and effective treatment. Recently, many machine learning or deep learning based methods have been proposed to first localize the pathology-related brain regions and then extract respective features for MCI progression diagnosis. However, the intrinsic relationship between pathological region localization and respective feature extraction was usually neglected. To address this issue, in this paper, we proposed a novel iterative attention focusing strategy for joint pathological region localization and identification of progressive MCI (pMCI) from stable MCI (sMCI). Moreover, by connecting diagnosis network and attention map generator, the pathological regions can be iteratively localized, and the respective diagnosis performance is in turn improved. Experiments on 393 training subjects from the ADNI-1 dataset and other 277 testing subjects from the ADNI-2 dataset show that our method can achieve 81.59% accuracy for pMCI vs. sMCI diagnosis. Our results outperform those with the state-of-the-art methods, while additionally providing a focused attention map on specific pathological locations related to MCI progression, i.e., left temporal lobe, entorhinal and hippocampus. This allows for more insights and better understanding of the progression of MCI to AD.
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Li, Q. et al. (2019). Novel Iterative Attention Focusing Strategy for Joint Pathology Localization and Prediction of MCI Progression. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_34
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DOI: https://doi.org/10.1007/978-3-030-32251-9_34
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