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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,982 papers · 148 categories

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0.3%0.5%0.8%0.1% · Dec 202419922001200920172026
4 results for “Neuropathology”
Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disordereess.IV

Combining brain structure and function for ASD diagnosis.

problem Identifying neuropathological bases of Autism Spectrum Disorder.
method Modeling brain structure as a graph, using rs-fMRI signals, and applying Graph Signal Processing.
result Decision tree outperforms state-of-the-art methods in diagnosing ASD.
Autism Spectrum DisorderGraph Signal ProcessingBrain StructureFunctional MRINeuropathology
2019-04-25abs ↗pdf ↗
Dynamic Classification of Latent Disease Progression with Auxiliary Surrogate Labelsstat.ME

Improves disease progression prediction using auxiliary surrogate labels and health markers.

problem Challenges in predicting disease progression due to unknown true disease states.
method Integrates hidden Markov model with time-varying discriminative classification model.
result Significant improvement in distinguishing LBD from AD using objective markers.
disease progressionsurrogate labelshealth markershidden Markov modeldiscriminative classification
2024-12-11abs ↗pdf ↗
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementiacs.LG

EBM uses high-dimensional imaging biomarkers to improve dementia progression estimation.

problem Current EBMs only use scalar biomarkers, limiting accuracy from cross-sectional data.
method Proposes nDEBM, a novel method using semi-supervised SVM on voxel-wise imaging biomarkers.
result nDEBM outperforms state-of-the-art EBM methods using regional volume biomarkers.
event-based modelingdementianeurodegenerationhigh-dimensional imagingsupport vector machine
2019-03-08abs ↗pdf ↗
Learning Common Harmonic Waves on Stiefel Manifold -- A New Mathematical Approach for Brain Network Analyseseess.IV

A new mathematical approach detects frequency-based alterations in brain networks.

problem Understanding disease-relevant brain alterations through network analysis.
method Proposes a novel connectome harmonic analysis framework using common harmonic waves learned from Stiefel manifolds.
result Identifies more significant and reproducible network dysfunction patterns in Alzheimer's disease.
brain networksharmonic analysisStiefel manifoldmanifold optimizationAlzheimer's disease
2020-07-01abs ↗pdf ↗