AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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High-resolution deep learning reconstruction for coronary CTA: compared efficacy of stenosis evaluation with other methods at in vitro and in vivo studies.
European radiology
Feb 4, 2025
OBJECTIVE: To directly compare coronary arterial stenosis evaluations by hybrid-type iterative reconstruction (IR), model-based IR (MBIR), deep learning reconstruction (DLR), and high-resolution deep learning reconstruction (HR-DLR) on coronary compu...
Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection.
IEEE transactions on medical imaging
Feb 4, 2025
Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained...
Machine Learning-Based CT Radiomics Model to Predict the Risk of Hip Fragility Fracture.
Academic radiology
Feb 3, 2025
RATIONALE AND OBJECTIVES: This research aimed to develop a combined model based on proximal femur attenuation values and radiomics features at routine CT to predict hip fragility fracture using machine learning methods.
Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer.
BMC medical imaging
Feb 3, 2025
Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of li...
Ultra-low-dose coronary CT angiography via super-resolution deep learning reconstruction: impact on image quality, coronary plaque, and stenosis analysis.
European radiology
Feb 1, 2025
OBJECTIVES: To exploit the capability of super-resolution deep learning reconstruction (SR-DLR) to save radiation exposure from coronary CT angiography (CCTA) and assess its impact on image quality, coronary plaque quantification and characterization...
Practical X-ray gastric cancer diagnostic support using refined stochastic data augmentation and hard boundary box training.
Artificial intelligence in medicine
Feb 1, 2025
Endoscopy is widely used to diagnose gastric cancer and has a high diagnostic performance, but it must be performed by a physician, which limits the number of people who can be diagnosed. In contrast, gastric X-rays can be taken by radiographers, thu...
Adjacent point aided vertebral landmark detection and Cobb angle measurement for automated AIS diagnosis.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 30, 2025
Adolescent Idiopathic Scoliosis (AIS) is a prevalent structural deformity disease of human spine, and accurate assessment of spinal anatomical parameters is essential for clinical diagnosis and treatment planning. In recent years, significant progres...
Super-resolution deep-learning reconstruction with 1024 matrix improves CT image quality for pancreatic ductal adenocarcinoma assessment.
European journal of radiology
Jan 30, 2025
OBJECTIVES: To evaluate the efficiency of super-resolution deep-learning reconstruction (SR-DLR) optimized for helical body imaging in assessing pancreatic ductal adenocarcinoma (PDAC) using normal-resolution (NR) CT scanner.
The use of artificial intelligence to aid the diagnosis of lung cancer - A retrospective-cohort study.
Radiography (London, England : 1995)
Jan 30, 2025
INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown success in early detection of lung cancer (LC), however, there remains uncertainty related to clinical ...
The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.
European journal of radiology
Jan 29, 2025
PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...
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