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| http://dx.doi.org/10.3389/fnins.2021.671836 | DOI Listing |
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A lightweight generative model for interpretable subject-level prediction.
Med Image Anal
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Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Department of Neuroscience and Biomedical Engineering, Aalto University, Finland; Department of Computer Science, Aalto University, Finland.
Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable.
View Article and Find Full Text PDFBeyond the Use: The Paradox of Scientific Animal Utilization.
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Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, University of Sherbrooke, Sherbrooke, QC, Canada.
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- Traditional Diffusion Tensor Imaging (DTI) approaches struggle with issues like crossing fibers and lesions, which limit the accuracy of tractography results.
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Multi-view fusion of diffusion MRI microstructural models: a preterm birth study.
Front Neurosci
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Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Objective: High Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural diffusion models for investigating preterm birth in order to identify non-invasive markers of altered white matter development.
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