Head Trauma

[A study on post-traumatic stress disorder classification based on multi-atlas multi-kernel graph convolutional network].

Post-traumatic stress disorder (PTSD) presents with complex and diverse clinical manifestations, making accurate and objective diagnosis challenging when relying solely on clinical assessments. Therefore, there is an urgent need to develop reliable and objective auxiliary diagnostic models to provide effective diagnosis for PTSD patients. Currently, the application of graph neural networks for rep...

Accelerating Post-Tornado Disaster Assessment Using Advanced Deep Learning Models

Post-disaster assessments of buildings and infrastructure are crucial for both immediate recovery efforts and long-term resilience planning. This research introduces an innovative approach to automating post-disaster assessments through advanced deep learning models. Our proposed system employs state-of-the-art computer vision techniques (YOLOv11 and ResNet50) to rapidly analyze images and video...

Neuromorphic Spiking Neural Network Based Classification of COVID-19 Spike Sequences

The availability of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) virus data post-COVID has reached exponentially to an enormous magn...

Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers

Due to its efficiency, Post-Training Quantization (PTQ) has been widely adopted for compressing Vision Transformers (ViTs). However, when quantized ...

I0T: Embedding Standardization Method Towards Zero Modality Gap

Contrastive Language-Image Pretraining (CLIP) enables zero-shot inference in downstream tasks such as image-text retrieval and classification. Howev...

Sentiment and Hashtag-aware Attentive Deep Neural Network for Multimodal Post Popularity Prediction

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of ...

Artificial Intelligence in Mental Health and Well-Being: Evolution, Current Applications, Future Challenges, and Emerging Evidence

Artificial Intelligence (AI) is a broad field that is upturning mental health care in many ways, from addressing anxiety, depression, and stress to ...

Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks

Deep hash-based retrieval techniques are widely used in facial retrieval systems to improve the efficiency of facial matching. However, it also carr...

Unified HT-CNNs Architecture: Transfer Learning for Segmenting Diverse Brain Tumors in MRI from Gliomas to Pediatric Tumors

Accurate segmentation of brain tumors from 3D multimodal MRI is vital for diagnosis and treatment planning across diverse brain tumors. This paper a...

Graph convolutional networks enable fast hemorrhagic stroke monitoring with electrical impedance tomography

Objective: To develop a fast image reconstruction method for stroke monitoring with electrical impedance tomography with image quality comparable to...

A Pipeline and NIR-Enhanced Dataset for Parking Lot Segmentation

Discussions of minimum parking requirement policies often include maps of parking lots, which are time consuming to construct manually. Open source ...

Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance

Mental health disorders are increasingly prevalent worldwide, creating an urgent need for innovative tools to support early diagnosis and interventi...

Take Fake as Real: Realistic-like Robust Black-box Adversarial Attack to Evade AIGC Detection

The security of AI-generated content (AIGC) detection is crucial for ensuring multimedia content credibility. To enhance detector security, research...

Multiclass Post-Earthquake Building Assessment Integrating Optical and SAR Satellite Imagery, Ground Motion, and Soil Data with Transformers

Timely and accurate assessments of building damage are crucial for effective response and recovery in the aftermath of earthquakes. Conventional pre...

Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation

Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurement...

Benchmarking Attention Mechanisms and Consistency Regularization Semi-Supervised Learning for Post-Flood Building Damage Assessment in Satellite Images

Post-flood building damage assessment is critical for rapid response and post-disaster reconstruction planning. Current research fails to consider t...

If Eleanor Rigby Had Met ChatGPT: A Study on Loneliness in a Post-LLM World

Loneliness, or the lack of fulfilling relationships, significantly impacts a person's mental and physical well-being and is prevalent worldwide. Pre...

Prediction of Post Traumatic Epilepsy Using MR-Based Imaging Markers.

Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Despite the high prevalence of...

ML-Based Framework to Predict the Severity of the Symptomatology in Patients with Post-Acute COVID-19 Syndrome.

The paper describes a cohort of patients with post-acute COVID-19 syndrome, evaluated for the first time between week 3 and week 12 from the onset of ...

Deep operator network models for predicting post-burn contraction

Burn injuries present a significant global health challenge. Among the most severe long-term consequences are contractures, which can lead to functi...