Predicting conversion of mild cognitive impairments to alzheimer’s disease and exploring impact of neuroimaging

The Alzheimer’s Disease Neuroimaging Initiative

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    15 Citations (Scopus)

    Abstract

    Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimers Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit on the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does have an effect on conversion prediction, however cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is ACC = 0.76 ± 0.01 and the area under the ROC curve – ROC AUC = 0.86 ± 0.01.

    Original languageEnglish
    Title of host publicationGraphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities - 2nd International Workshop, GRAIL 2018 and 1st International Workshop, Beyond MIC 2018 Held in Conjunction with MICCAI 2018, Proceedings
    EditorsDanail Stoyanov, Aristeidis Sotiras, Bartlomiej Papiez, Adrian V. Dalca, Anne Martel, Sarah Parisot, Enzo Ferrante, Lena Maier-Hein, Mert R. Sabuncu, Li Shen, Zeike Taylor
    PublisherSpringer Verlag
    Pages83-91
    Number of pages9
    ISBN (Print)9783030006884
    DOIs
    Publication statusPublished - 2018
    Event2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
    Duration: 20 Sep 201820 Sep 2018

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11044 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
    Country/TerritorySpain
    CityGranada
    Period20/09/1820/09/18

    Keywords

    • CNN
    • Disease progression
    • Image classification
    • MRI
    • Similarity learning

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