Differential dementia diagnosis on incomplete data with latent trees

Christian Ledig, Sebastian Kaltwang, Antti Tolonen, Juha Koikkalainen, Philip Scheltens, Frederik Barkhof, Hanneke Rhodius-Meester, Betty Tijms, Afina W. Lemstra, Wiesje van der Flier, Jyrki Lötjönen, Daniel Rueckert

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

    1 Citation (Scopus)


    Incomplete patient data is a substantial problem that is not sufficiently addressed in current clinical research. Many published methods assume both completeness and validity of study data. However,this assumption is often violated as individual features might be unavailable due to missing patient examination or distorted/wrong due to inaccurate measurements or human error. In this work we propose to use the Latent Tree (LT) generative model to address current limitations due to missing data. We show on 491 subjects of a challenging dementia dataset that LT feature estimation is more robust towards incomplete data as compared to mean or Gaussian Mixture Model imputation and has a synergistic effect when combined with common classifiers (we use SVM as example). We show that LTs allow the inclusion of incomplete samples into classifier training. Using LTs,we obtain a balanced accuracy of 62% for the classification of all patients into five distinct dementia types even though 20% of the features are missing in both training and testing data (68% on complete data). Further,we confirm the potential of LTs to detect outlier samples within the dataset.
    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention
    Subtitle of host publicationMICCAI 2016
    ISBN (Electronic)978-3-319-46723-8
    ISBN (Print)978-3-319-46722-1
    Publication statusPublished - 2 Oct 2016
    MoE publication typeA4 Article in a conference publication
    Event19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
    Duration: 17 Oct 201621 Oct 2016

    Publication series

    SeriesLecture Notes in Computer Science


    Conference19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
    Abbreviated titleMICCAI 2016


    • Latent Trees
    • differential diagnosis
    • dementia
    • incomplete data


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