ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2021
Deep Learning on SDF for Classifying Brain Biomarkers.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.Network neuroscience (Cambridge, Mass.) · 2025Article
- Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.Journal of Alzheimer's disease : JAD · 2023Article
- In the Face of a Pandemic: "I Felt the Same as When the War Started"-A Qualitative Study on COVID-19 Vaccine Acceptance in Bosnia and Herzegovina.International journal of public health · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an important indicator for the disease. However, current methods to detect Aβ pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Thus a less invasive and cheaper approach is demanded. MRI which has been used widely in preclinical AD has recently shown the capability to predict brain Aβ positivity. This motivates us to develop a method, SDF sparse convolution, taking MRI to predict Aβ positivity. We obtain subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and use our method to discriminate Aβ positivity. Theoretically, we provide analysis towards the understanding of what the network has learned. Empirically, it shows strong performance on par or even better than state of the art.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.