Automated diagnosis of Alzheimer's disease using image similarity and user feedback

In this work, we present a learning framework to help early diagnosis of Alzheimer's disease (AD) from magnetic resonance images using visual similarity and user feedback. Our approach relies on a nearest neighbor (NN) procedure where the similarity measure is obtained via on-line supervised learning. This framework differs from standard classification based medical diagnosis in that learning is always carried out on-line with a small training set, much like in relevance feedback-driven retrieval. We propose two alternative approaches to learn the similarities between cases. While the first approach indirectly employs the distance to support vector machine decision boundary as a similarity measure, the second one aims at directly finding a similarity function based on the minimization of the empirical ranking risk. Several experiments on Open Access Series of Imaging Studies neuroimaging database establish that, even with weak global visual descriptors and small training sets, this framework has better diagnostic performance than standard classification based approaches and it also enjoys a certain degree of robustness against incorrect relevance judgments.