Head Trauma

A Category-Fragment Segmentation Framework for Pelvic Fracture Segmentation in X-ray Images

Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Imaging techniques such as CT and 2D X-ray imaging are used to transfer the surgical plan to the operating room through image registration, enabling quick intraoperative adjustments. Specifically, segmenting pelvic fractures from 2D X-ray imaging can assist in accurately positioning bone fragments and...

DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization

Natural disasters increasingly threaten communities worldwide, creating an urgent need for rapid, reliable building damage assessment to guide emergency response and recovery efforts. Current methods typically classify damage in binary (damaged/undamaged) or ordinal severity terms, limiting their practical utility. In fact, the determination of damage typology is crucial for response and recover...

Hyperlocal disaster damage assessment using bi-temporal street-view imagery and pre-trained vision models

Street-view images offer unique advantages for disaster damage estimation as they capture impacts from a visual perspective and provide detailed, on...

Are We Merely Justifying Results ex Post Facto? Quantifying Explanatory Inversion in Post-Hoc Model Explanations

Post-hoc explanation methods provide interpretation by attributing predictions to input features. Natural explanations are expected to interpret how...

The Lyme Disease Controversy: An AI-Driven Discourse Analysis of a Quarter Century of Academic Debate and Divides

The scientific discourse surrounding Chronic Lyme Disease (CLD) and Post-Treatment Lyme Disease Syndrome (PTLDS) has evolved over the past twenty-fi...

SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis

Despite significant progress in intelligent fault diagnosis (IFD), the lack of interpretability remains a critical barrier to practical industrial a...

Detecting PTSD in Clinical Interviews: A Comparative Analysis of NLP Methods and Large Language Models

Post-Traumatic Stress Disorder (PTSD) remains underdiagnosed in clinical settings, presenting opportunities for automated detection to identify pati...

Multimodal LLMs for OCR, OCR Post-Correction, and Named Entity Recognition in Historical Documents

We explore how multimodal Large Language Models (mLLMs) can help researchers transcribe historical documents, extract relevant historical informatio...

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...

DiTFastAttnV2: Head-wise Attention Compression for Multi-Modality Diffusion Transformers

Text-to-image generation models, especially Multimodal Diffusion Transformers (MMDiT), have shown remarkable progress in generating high-quality ima...

Post-Incorporating Code Structural Knowledge into LLMs via In-Context Learning for Code Translation

Code translation migrates codebases across programming languages. Recently, large language models (LLMs) have achieved significant advancements in s...

Improving Quantization with Post-Training Model Expansion

The size of a model has been a strong predictor of its quality, as well as its cost. As such, the trade-off between model cost and quality has been ...

Post-composing ontology terms for efficient phenotyping in plant breeding.

Ontologies are widely used in databases to standardize data, improving data quality, integration, and ease of comparison. Within ontologies tailored t...

Free-Lunch Color-Texture Disentanglement for Stylized Image Generation

Recent advances in Text-to-Image (T2I) diffusion models have transformed image generation, enabling significant progress in stylized generation usin...

ACT360: An Efficient 360-Degree Action Detection and Summarization Framework for Mission-Critical Training and Debriefing

Effective training and debriefing are critical in high-stakes, mission-critical environments such as disaster response, military simulations, and in...

When neural implant meets multimodal LLM: A dual-loop system for neuromodulation and naturalistic neuralbehavioral research

We propose a novel dual-loop system that synergistically combines responsive neurostimulation (RNS) implants with artificial intelligence-driven wea...

LHM: Large Animatable Human Reconstruction Model from a Single Image in Seconds

Animatable 3D human reconstruction from a single image is a challenging problem due to the ambiguity in decoupling geometry, appearance, and deforma...

Markerless Tracking-Based Registration for Medical Image Motion Correction

Our study focuses on isolating swallowing dynamics from interfering patient motion in videofluoroscopy, an X-ray technique that records patients swa...

Deformable Registration Framework for Augmented Reality-based Surgical Guidance in Head and Neck Tumor Resection

Head and neck squamous cell carcinoma (HNSCC) has one of the highest rates of recurrence cases among solid malignancies. Recurrence rates can be red...

Periodontal Bone Loss Analysis via Keypoint Detection With Heuristic Post-Processing

Calculating percentage bone loss is a critical test for periodontal disease staging but is sometimes imprecise and time consuming when manually calc...