Using multiparametric data with missing features for learning patterns of pathology.

Registration Now Open for

ADOS-2 Clinical Training Workshop

November 25-27. Click here for info!

TitleUsing multiparametric data with missing features for learning patterns of pathology.
Publication TypeJournal Article
Year of Publication2012
AuthorsIngalhalikar, M, Parker, WA, Bloy, L, Roberts, TPL, Verma, R
JournalMed Image Comput Comput Assist Interv
Volume15
IssuePt 3
Pagination468-75
Date Published2012
KeywordsAdolescent, Algorithms, Brain, Brain Mapping, Child, Child Development Disorders, Pervasive, Child, Preschool, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Infant, Infant, Newborn, Magnetoencephalography, Male, Pattern Recognition, Automated, Subtraction Technique
Abstract

The paper presents a method for learning multimodal classifiers from datasets in which not all subjects have data from all modalities. Usually, subjects with a severe form of pathology are the ones failing to satisfactorily complete the study, especially when it consists of multiple imaging modalities. A classifier capable of handling subjects with unequal numbers of modalities prevents discarding any subjects, as is traditionally done, thereby broadening the scope of the classifier to more severe pathology. It also allows design of the classifier to include as much of the available information as possible and facilitates testing of subjects with missing modalities over the constructed classifier. The presented method employs an ensemble based approach where several subsets of complete data are formed and trained using individual classifiers., The output from these classifiers is fused using a weighted aggregation step giving an optimal probabilistic score for each subject. The method is applied to a spatio-temporal dataset for autism spectrum disorders (ASD) (96 patients with ASD and 42 typically developing controls) that consists of functional features from magnetoencephalography (MEG) and structural connectivity features from diffusion tensor imaging (DTI). A clear distinction between ASD and controls is obtained with an average 5-fold accuracy of 83.3% and testing accuracy of 88.4%. The fusion classifier performance is superior to the classification achieved using single modalities as well as multimodal classifier using only complete data (78.3%). The presented multimodal classifier framework is applicable to all modality combinations.

Alternate JournalMed Image Comput Comput Assist Interv
PubMed ID23286164
PubMed Central IDPMC4023481
Grant ListR01 DC008871 / DC / NIDCD NIH HHS / United States
DC008871 / DC / NIDCD NIH HHS / United States
R01 MH092862 / MH / NIMH NIH HHS / United States
MH092862 / MH / NIMH NIH HHS / United States
R01 MH079938 / MH / NIMH NIH HHS / United States
MH079938 / MH / NIMH NIH HHS / United States
Comments
Leave a Comment