title: Multiple instance learning for classification of dementia in brain MRI.
abstract: Machine learning techniques have been widely used to support the diagnosis of neurological diseases such as dementia. Recent approaches utilize local intensity patterns within patches to derive voxelwise grading measures of disease. However, the relationships among these patches are usually ignored. In addition, there is some ambiguity in assigning disease labels to the extracted patches. Not all of the patches extracted from patients with dementia are characteristic of morphology associated with disease. In this paper, we propose to use a multiple instance learning method to address the problem of assigning training labels to the patches. In addition, a graph is built for each image to exploit the relationships among these patches, which aids the classification work. We illustrate the proposed approach in an application for the detection of Alzheimer's disease (AD): Using the baseline MR images of 834 subjects from the ADNI study, the proposed method can achieve a classification accuracy of 88.8% between AD patients and healthy controls, and 69.6% between patients with stable Mild Cognitive Impairment (MCI) and progressive MCI. These results compare favourably with state-of-the-art classification methods.
authors.affiliation: Department of Computing, Imperial College London, London, UK. t.tongll@imperial.ac.uk
authors.forename: Tong
authors.initials: T
authors.lastname: Tong
authors.affiliation: Department of Computing, Imperial College London, London, UK.
authors.forename: Robin
authors.initials: R
authors.lastname: Wolz
authors.affiliation: Department of Computing, Imperial College London, London, UK.
authors.forename: Qinquan
authors.initials: Q
authors.lastname: Gao
authors.affiliation: Center for the Developing Brain, Division of Imaging Sciences and Biomedical, Engineering, King's College London, St. Thomas Hospital, London, UK.
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.affiliation: Department of Computing, Imperial College London, London, UK.
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D000544:Alzheimer Disease / Q000150:complications / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D003704:Dementia / Q000150:complications / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity