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Assumption-Lean Post-Integrated Inference with Negative Control Outcomes

Data integration methods aim to extract low-dimensional embeddings from high-dimensional outcomes to remove unwanted variations, such as batch effects and unmeasured covariates, across heterogeneous datasets. However, multiple hypothesis testing after integration can be biased due to data-dependent processes. We introduce a robust post-integrated inference (PII) method that adjusts for latent he...

Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities

Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain abnormalities is essential for successful neurosurgical intervention. Current clinical approaches to identify structural abnormalities for surgical targeting in epilepsy do not use diffusion-weighted MRI (dMRI), despite evidence that dMRI abnormaliti...

From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals

Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) ex...

Examining the Role of Relationship Alignment in Large Language Models

The rapid development and deployment of Generative AI in social settings raise important questions about how to optimally personalize them for users...

A Thematic Framework for Analyzing Large-scale Self-reported Social Media Data on Opioid Use Disorder Treatment Using Buprenorphine Product

Background: One of the key FDA-approved medications for Opioid Use Disorder (OUD) is buprenorphine. Despite its popularity, individuals often report...

Precise Workcell Sketching from Point Clouds Using an AR Toolbox

Capturing real-world 3D spaces as point clouds is efficient and descriptive, but it comes with sensor errors and lacks object parametrization. These...

Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Review

Artificial Intelligence (AI) has demonstrated significant potential in automating various medical imaging tasks, which could soon become routine in ...

Clinical Validation of a Real-Time Machine Learning-based System for the Detection of Acute Myeloid Leukemia by Flow Cytometry

Machine-learning (ML) models in flow cytometry have the potential to reduce error rates, increase reproducibility, and boost the efficiency of clini...

Gradient-free Post-hoc Explainability Using Distillation Aided Learnable Approach

The recent advancements in artificial intelligence (AI), with the release of several large models having only query access, make a strong case for e...

A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia

We propose a lesion-aware graph neural network (LEGNet) to predict language ability from resting-state fMRI (rs-fMRI) connectivity in patients with ...

Interpretable breast cancer classification using CNNs on mammographic images

Deep learning models have achieved promising results in breast cancer classification, yet their 'black-box' nature raises interpretability concerns....

Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligence

Deep learning techniques have revolutionized image classification by mimicking human cognition and automating complex decision-making processes. How...

Enhancing Pulmonary Embolism Detection in COVID-19 Patients Through Advanced Deep Learning Techniques.

The intersection of COVID-19 and pulmonary embolism (PE) has posed unprecedented challenges in medical diagnostics. The critical nature of PE and its ...

Causal Deep Learning for the Detection of Adverse Drug Reactions: Drug-Induced Acute Kidney Injury as a Case Study.

Causal Deep/Machine Learning (CDL/CML) is an emerging Artificial Intelligence (AI) paradigm. The combination of causal inference and AI could mine exp...

MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance

In the era of Large Language Models (LLMs), given their remarkable text understanding and generation abilities, there is an unprecedented opportunit...

What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers

Customers' reviews and feedback play crucial role on electronic commerce~(E-commerce) platforms like Amazon, Zalando, and eBay in influencing other ...

Multimodal Unlearnable Examples: Protecting Data against Multimodal Contrastive Learning

Multimodal contrastive learning (MCL) has shown remarkable advances in zero-shot classification by learning from millions of image-caption pairs cra...

Assessing the Feasibility, and Efficacy of Virtual Reality Navigational Training for Older Adults

Objective. Evaluate the feasibility of Virtual Reality (VR) wayfinding training with aging adults, and examine the impact of the training on wayfind...

Standard compliant video coding using low complexity, switchable neural wrappers

The proliferation of high resolution videos posts great storage and bandwidth pressure on cloud video services, driving the development of next-gene...

Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization

With the rapid advances in deep learning and smart manufacturing in Industry 4.0, there is an imperative for high-throughput, high-performance, and ...