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Surgical resection of portions of the temporal lobe is the standard of care for patients with refractory mesial temporal lobe epilepsy. While this reduces seizures, it often results in an inability to form new memories, which leads to difficulties in social situations, learning, and suboptimal quality of life. Learning about the success or failure to form new memory in such patients is critical if...
Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are not equally atrophied during...
Traditional task-based fMRI activation detection methods, such as the general linear model (GLM), assume that the fMRI signals of activated brain regions follow the external stimulus paradigm. Typically, these activated regions are detected independently in a voxel-wise fashion, and the interaction among voxels is nevertheless neglected. Despite the wide use and remarkable success of GLM, the temporal...
We propose a convolutional sparse dictionary learning and coding approach for detecting and counting instances of a repeated object in a holographic lens-free image. The proposed approach exploits the fact that an image containing a single object instance can be approximated as the convolution of a (small) object template with a spike at the location of the object instance. Therefore, an image containing...
Resting-state functional magnetic resonance imaging (rs-fMRI) has become a powerful technique for analyzing cognitive function and its disruption in mental diseases, including schizophrenia. It has played an important role in analyzing and diagnosing mental diseases. In this paper, we present a novel machine learning approach called group-wise sparse representation of rs-fMRI signals to find differences...
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