Orthopedics

Landmarks Are Alike Yet Distinct: Harnessing Similarity and Individuality for One-Shot Medical Landmark Detection

Landmark detection plays a crucial role in medical imaging applications such as disease diagnosis, bone age estimation, and therapy planning. However, training models for detecting multiple landmarks simultaneously often encounters the "seesaw phenomenon", where improvements in detecting certain landmarks lead to declines in detecting others. Yet, training a separate model for each landmark incr...

Text-Driven Diffusion Model for Sign Language Production

We introduce the hfut-lmc team's solution to the SLRTP Sign Production Challenge. The challenge aims to generate semantically aligned sign language pose sequences from text inputs. To this end, we propose a Text-driven Diffusion Model (TDM) framework. During the training phase, TDM utilizes an encoder to encode text sequences and incorporates them into the diffusion model as conditional input to...

VideoRFSplat: Direct Scene-Level Text-to-3D Gaussian Splatting Generation with Flexible Pose and Multi-View Joint Modeling

We propose VideoRFSplat, a direct text-to-3D model leveraging a video generation model to generate realistic 3D Gaussian Splatting (3DGS) for unboun...

Impact of pH and chloride content on the biodegradation of magnesium alloys for medical implants: An in vitro and phase-field study

The individual contributions of pH and chloride concentration to the corrosion kinetics of bioabsorbable magnesium (Mg) alloys remain unresolved des...

DPFAGA-Dynamic Power Flow Analysis and Fault Characteristics: A Graph Attention Neural Network

We propose the joint graph attention neural network (GAT), clustering with adaptive neighbors (CAN) and probabilistic graphical model for dynamic po...

Submillimeter-Accurate 3D Lumbar Spine Reconstruction from Biplanar X-Ray Images: Incorporating a Multi-Task Network and Landmark-Weighted Loss

Three-dimensional reconstruction of the spine under weight-bearing conditions from biplanar X-ray images is of great importance for the clinical ass...

Semantic Communication in Dynamic Channel Scenarios: Collaborative Optimization of Dual-Pipeline Joint Source-Channel Coding and Personalized Federated Learning

Semantic communication is designed to tackle issues like bandwidth constraints and high latency in communication systems. However, in complex networ...

Shift, Scale and Rotation Invariant Multiple Object Detection using Balanced Joint Transform Correlator

The Polar Mellin Transform (PMT) is a well-known technique that converts images into shift, scale and rotation invariant signatures for object detec...

Interpretable Unsupervised Joint Denoising and Enhancement for Real-World low-light Scenarios

Real-world low-light images often suffer from complex degradations such as local overexposure, low brightness, noise, and uneven illumination. Super...

Patient-specific radiomic feature selection with reconstructed healthy persona of knee MR images

Classical radiomic features have been designed to describe image appearance and intensity patterns. These features are directly interpretable and re...

Adaptive Transformer Attention and Multi-Scale Fusion for Spine 3D Segmentation

This study proposes a 3D semantic segmentation method for the spine based on the improved SwinUNETR to improve segmentation accuracy and robustness....

Learning Dual-Domain Multi-Scale Representations for Single Image Deraining

Existing image deraining methods typically rely on single-input, single-output, and single-scale architectures, which overlook the joint multi-scale...

Assessing the effects of immune checkpoint inhibitors on bone utilizing machine learning-assisted opportunistic quantitative computed tomography.

Immune checkpoint inhibitors (ICIs) are widely used in cancer treatment, yet their impact on bone health remains unclear. This study aimed to perform ...

[Research progress in application of intelligent remote follow-up mode in hip and knee arthroplasty].

OBJECTIVE: To review the research progress of intelligent remote follow-up modes in the application after hip and knee arthroplasty.

Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning

Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, b...

AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI

Background: Accurate spinal structure measurement is crucial for assessing spine health and diagnosing conditions like spondylosis, disc herniation,...

Multi-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations

Accurate survival prediction in oncology requires integrating diverse imaging modalities to capture the complex interplay of tumor biology. Traditio...

Teaching LMMs for Image Quality Scoring and Interpreting

Image quality scoring and interpreting are two fundamental components of Image Quality Assessment (IQA). The former quantifies image quality, while ...

Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data

Automated knee cartilage segmentation using point-of-care ultrasound devices and deep-learning networks has the potential to enhance the management ...

Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition

Few-shot Action Recognition (FSAR) constitutes a crucial challenge in computer vision, entailing the recognition of actions from a limited set of ex...