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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.

169,291 papers · 148 categories

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9 results for “aneurysm”

Aneurysm Growth Time Series Reconstruction Using Physics-informed Autoencoderq-bio.QM

Method reconstructs aneurysm growth history from patient parameters using physics-informed autoencoder.

problem Predicting arterial aneurysm rupture due to inaccessible growth time series.

method Physics-informed autoencoder combined with neural network for mapping patient parameters to aneurysm growth time history.

result Incorporating physical model constraints improves time series reconstruction, especially in noisy data.

aneurysm growth autoencoder neural network physics-informed time series reconstruction

2025-10-05abs ↗pdf ↗

IntrA: 3D Intracranial Aneurysm Dataset for Deep Learningeess.IV

3D dataset for intracranial aneurysms aids deep learning applications.

problem Lack of 3D medical datasets for deep learning.

method Developed an open-access 3D dataset, IntrA, for intracranial aneurysms.

result Demonstrated the challenges and performance of 3D medical datasets.

3D dataset intracranial aneurysms deep learning medical applications classification

2020-03-02abs ↗pdf ↗

A neural network model that learns differences in diagnosis strategies among radiologists has an improved area under the curve for aneurysm status classification in magnetic resonance angiography image serieseess.IV

Neural network improves aneurysm classification accuracy in MRI images.

problem Classifying aneurysm status in MRI images with varying radiologist expertise.

method Constructed neural network trained on radiologists' annotations, compared to a control model.

result Proposed model had a higher area under the curve (0.845 vs. 0.793).

neural networks MRI neuroradiology classification accuracy deep learning

2020-02-03abs ↗pdf ↗

Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networkscs.LG

ZerNet improves aneurysm wall stress estimation.

problem Estimating wall stress of cerebral aneurysms using ConvNets.

method ZerNet, a geometric ConvNet that generalizes convolution and pooling operations on manifolds.

result ZerNet outperforms state-of-the-art geometric ConvNets in aneurysm wall stress estimation.

aneurysm wall stress ConvNet Zernike manifold

2018-06-20abs ↗pdf ↗

Enhanced Vascular Flow Simulations in Aortic Aneurysm via Physics-Informed Neural Networks and Deep Operator Networkscs.LG

New algorithms improve vascular flow simulations in aortic aneurysms.

problem Limited accuracy of MRI in hemodynamics, patient-specific flow boundary conditions, and CFD's computational demands.

method Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets) integrated with 3D Navier-Stokes equations.

result Improved computational efficiency and good agreement with CFD simulations.

Physics-Informed Neural Networks Deep Operator Networks 3D Navier-Stokes equations Aortic Aneurysm Vascular Flow Simulations

2025-03-19abs ↗pdf ↗

Causal Graph Aided Causal Discovery in an Observational Aneurysmal Subarachnoid Hemorrhage Studystat.ME

Causal graph aids observational study insights in aSAH patients.

problem Lack of clear objectives and tools for identifying necessary adjustments in observational studies.

method Uses causal directed acyclic graphs (DAGs) to provide insights mid-study and identify necessary data enhancements.

result Midway insights and necessary data enhancements identified for meaningful causal questions.

causal inference observational studies aneurysmal subarachnoid hemorrhage DAGs midway insights

2024-08-12abs ↗pdf ↗

A Fully Automated Pipeline Using Swin Transformers for Deep Learning-Based Blood Segmentation on Head CT Scans After Aneurysmal Subarachnoid Hemorrhagecs.CV

AI tool automates blood segmentation from head CT scans after SAH.

problem Accurate volumetric assessment of SAH patients for clinical and prognostic implications.

method Transformer-based Swin UNETR architecture for noncontrast CT scans.

result High accuracy and robust performance across internal and external validation cohorts.

AI Subarachnoid Hemorrhage Blood Segmentation Transformer UNETR

2023-12-29abs ↗pdf ↗

Distributional Deep Learning for Super-Resolution of 4D Flow MRI under Domain Shiftcs.CV

New deep learning method improves 4D Flow MRI super-resolution under domain shift.

problem Domain shift in low-resolution 4D Flow MRI data.

method Distributional deep learning framework for domain generalization.

result Framework significantly outperforms traditional methods in real data applications.

Deep Learning Super-Resolution Domain Shift 4D Flow MRI Medical Imaging

2026-02-16abs ↗pdf ↗

Hidden Fluid Mechanics: A Navier-Stokes Informed Deep Learning Framework for Assimilating Flow Visualization Datacs.CE

HFM uses deep learning to infer fluid dynamics from visual data.

problem Data assimilation of fluid dynamics from flow visualizations.

method Physics-informed deep learning framework based on Navier-Stokes equations.

result Accurate predictions of velocity and pressure fields in complex flows.

Navier-Stokes equations deep learning fluid dynamics flow visualization data assimilation

2018-08-13abs ↗pdf ↗