ArticleInformation processing in medical imaging : proceedings of the ... conference2017
Kernel Methods for Riemannian Analysis of Robust Descriptors of the Cerebral Cortex.
Article in Information processing in medical imaging : proceedings of the ... conference, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Differential Diagnosis of Frontotemporal Dementia, Alzheimer's Disease, and Normal Aging Using a Multi-Scale Multi-Type Feature Generative Adversarial Deep Neural Network on Structural Magnetic Resonance Images.Frontiers in neuroscience · 2020Article
- Alteration of the Cortex Shape as a Proxy of White Matter Swelling in Severe Cerebral Small Vessel Disease.Frontiers in neurology · 2019Article
- Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.IEEE transactions on medical imaging · 2018Article
- A Comparative Atlas-Based Recognition of Mild Cognitive Impairment With Voxel-Based Morphometry.Frontiers in neuroscience · 2018Article
- Enhanced Data Representation by Kernel Metric Learning for Dementia Diagnosis.Frontiers in neuroscience · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Typical cerebral cortical analyses rely on spatial normalization and are sensitive to misregistration arising from partial homologies between subject brains and local optima in nonlinear registration. In contrast, we use a descriptor of the 3D cortical sheet (jointly modeling folding and thickness) that is robust to misregistration. Our histogram-based descriptor lies on a
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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.