Strokes

One Pic is All it Takes: Poisoning Visual Document Retrieval Augmented Generation with a Single Image

Multimodal retrieval augmented generation (M-RAG) has recently emerged as a method to inhibit hallucinations of large multimodal models (LMMs) through a factual knowledge base (KB). However, M-RAG also introduces new attack vectors for adversaries that aim to disrupt the system by injecting malicious entries into the KB. In this work, we present a poisoning attack against M-RAG targeting visual ...

Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.

Background Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, requiring swift and precise clinical decisions based on neuroimaging. Recent advances in deep learning-based computer vision and language artificial intelligence (AI) models have demonstrated transformative performance for several stroke-related applications. Purpose To evaluate deep learning applications for imagi...

Automated Bi-Ventricular Segmentation and Regional Cardiac Wall Motion Analysis for Rat Models of Pulmonary Hypertension.

Artificial intelligence-based cardiac motion mapping offers predictive insights into pulmonary hypertension (PH) disease progression and its impact on...

Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Records

Objective: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performa...

Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

Early and accurate diagnosis of pulmonary hypertension (PH) is essential for optimal patient management. Differentiating between pre-capillary and p...

Identification of hypertension subtypes using microRNA profiles and machine learning.

OBJECTIVE: Hypertension is a major cardiovascular risk factor affecting about 1 in 3 adults. Although the majority of hypertension cases (∼90%) are cl...

Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease

Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG,...

Probing Network Decisions: Capturing Uncertainties and Unveiling Vulnerabilities Without Label Information

To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examination...

EvalTree: Profiling Language Model Weaknesses via Hierarchical Capability Trees

An ideal model evaluation should achieve two goals: identifying where the model fails and providing actionable improvement guidance. Toward these go...

Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers

Objective. Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed t...

SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models

The purpose of this study is to introduce SKG-LLM. A knowledge graph (KG) is constructed from stroke-related articles using mathematical and large l...

Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation

Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis by leveraging complementary information...

State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters

Efficient and accurate whole-brain lesion segmentation remains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-...

Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction

Data-centric artificial intelligence (AI) has remarkably advanced medical imaging, with emerging methods using synthetic data to address data scarci...

Multimodal Lead-Specific Modeling of ECG for Low-Cost Pulmonary Hypertension Assessment

Pulmonary hypertension (PH) is frequently underdiagnosed in low- and middle-income countries (LMICs) primarily due to the scarcity of advanced diagn...

Estimating Blood Pressure with a Camera: An Exploratory Study of Ambulatory Patients with Cardiovascular Disease

Hypertension is a leading cause of morbidity and mortality worldwide. The ability to diagnose and treat hypertension in the ambulatory population is...

Artificial Intelligence-Enhanced Electrocardiography for Prediction of Incident Hypertension.

IMPORTANCE: Hypertension underpins significant global morbidity and mortality. Early lifestyle intervention and treatment are effective in reducing ad...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires techn...

Sketch & Paint: Stroke-by-Stroke Evolution of Visual Artworks

Understanding the stroke-based evolution of visual artworks is useful for advancing artwork learning, appreciation, and interactive display. While t...

Automatic Temporal Segmentation for Post-Stroke Rehabilitation: A Keypoint Detection and Temporal Segmentation Approach for Small Datasets

Rehabilitation is essential and critical for post-stroke patients, addressing both physical and cognitive aspects. Stroke predominantly affects olde...