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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.

168,742 papers · 148 categories

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13 results for “Subarachnoid Hemorrhage”

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 ↗

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 ↗

Enhanced Mortality Prediction In Patients With Subarachnoid Haemorrhage Using A Deep Learning Model Based On The Initial CT Scancs.CV

Deep learning predicts SAH patient mortality from initial CT scans.

problem High mortality rates in SAH patients.

method CNN-based algorithm using transfer learning on CT scans.

result Model accurately predicts mortality (74% accuracy, 82% AUC).

SAH Deep Learning CT Scan Mortality Prediction Convolutional Neural Networks

2023-08-25abs ↗pdf ↗

Lesion Conditional Image Generation for Improved Segmentation of Intracranial Hemorrhage from CT Imageseess.IV

LcGAN generates synthetic CT images for hemorrhagic lesion segmentation.

problem Scarce training data for hemorrhagic lesion segmentation.

method Lesion conditional Generative Adversarial Network (LcGAN) for synthetic image generation.

result Segmentation improved by 12.8% with synthetic data augmentation.

Generative Adversarial Networks Data Augmentation Hemorrhagic Lesion Segmentation CT Image Generation Machine Learning

2020-03-30abs ↗pdf ↗

RADNET: Radiologist Level Accuracy using Deep Learning for HEMORRHAGE detection in CT Scanscs.CV

We describe a deep learning approach for automated brain hemorrhage detection from computed tomography (CT) scans. Our model emulates the procedure followed by radiologists to analyse a 3D CT scan in real-world. Similar to radiologists, the model sifts through 2D cross-sectional slices while paying close attention to p…

2017-10-13abs ↗pdf ↗

Identification of physiological shock in intensive care units via Bayesian regime switching modelsstat.AP

Bayesian model detects internal bleeding in ICU patients.

problem Late detection of internal bleeding in ICU patients.

method Bayesian regime switching model analyzing vitals and labs.

result Probabilistic assessment of patient's physiological state.

Bayesian regime switching internal bleeding ICU vitals laboratory

2026-03-23abs ↗pdf ↗

Train, Learn, Expand, Repeatcs.LG

Proposes a strategy to train models with minimal labeled data.

problem Scarce and expensive labeled data for medical tasks.

method Recursive training strategy to use image-level annotations for pixel-level segmentation.

result Improved segmentation of intracranial hemorrhage in CT scans.

machine learning medical imaging segmentation recursive training labeled data

2020-03-18abs ↗pdf ↗

Model Agnostic Combination for Ensemble Learningcs.LG

MAC combines models without locking them, improving ensemble performance.

problem Improving ensemble learning performance with flexibility.

method Model agnostic combination technique that dynamically combines models.

result MAC outperforms classical methods and competitive to boosting.

ensemble learning model agnostic flexible combination Kaggle RSNA Intracranial Hemorrhage Detection

2020-06-16abs ↗pdf ↗

Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learningcs.LG

Detects physiological patterns to hemodynamic stress using unsupervised deep learning.

problem Identify and characterize physiological responses to hemorrhage in raw vital sign data.

method Transform vital sign time series into latent space using unsupervised deep learning, identify clusters, and evaluate latent embeddings.

result Clusters in latent embeddings correspond to physiological response patterns matching physicians' intuition.

hemodynamic stress physiological response unsupervised learning deep learning vital sign data

2019-11-12abs ↗pdf ↗

Hyperbolic Secant representation of the logistic function: Application to probabilistic Multiple Instance Learning for CT intracranial hemorrhage detectioncs.LG

New GP-based MIL method using Hyperbolic Secant distribution.

problem Probabilistic MIL for medical image analysis.

method Formulate VGPMIL using Pólya-Gamma random variables.

result Competitive or superior predictive performance and efficiency.

Multiple Instance Learning Gaussian Processes Hyperbolic Secant Gamma distribution CT intracranial hemorrhage

2024-03-21abs ↗pdf ↗

Semi-Supervised Natural Language Approach for Fine-Grained Classification of Medical Reportscs.LG

Model uses unsupervised learning to classify medical reports with less labeled data.

problem Lack of labeled data for fine-grained disease classification in medical reports.

method Developed a pipeline combining an unsupervised encoder-language model and a supervised classifier model.

result Improved classification accuracy with less labeled data compared to previous methods.

natural language processing unsupervised learning medical report classification deep learning machine learning

2019-10-29abs ↗pdf ↗

Harmonic Mean Point Processes: Proportional Rate Error Minimization for Obtundation Predictionstat.ML

New method improves prediction accuracy for low-risk patients in healthcare.

problem Machine learning models often focus on high-risk patients, ignoring low-risk ones.

method Proposed a new log-likelihood formulation to minimize proportional rate error.

result Improved prediction accuracy for low-risk patients in EHR data.

point processes log-likelihood proportional rate error obtundation prediction healthcare risk stratification

2019-11-12abs ↗pdf ↗

Optimal transport weights for causal inferencestat.ME

A new method corrects bias in causal inference by balancing covariate distributions.

problem Bias in causal effect estimates due to covariate imbalance.

method Causal Optimal Transport, a nonparametric approach targeting distributional balance.

result Causal Optimal Transport outperforms other methods in misspecified models.

causal inference optimal transport covariate balance bias correction observational studies

2021-09-05abs ↗pdf ↗