AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Task based evaluation of sparse view CT reconstruction techniques for intracranial hemorrhage diagnosis using an AI observer model.
Scientific reports
Jul 17, 2025
Sparse-view computed tomography (CT) holds promise for reducing radiation exposure and enabling novel system designs. Traditional reconstruction algorithms, including Filtered Backprojection (FBP) and Model-Based Iterative Reconstruction (MBIR), ofte...
Image quality and radiation dose of reduced-dose abdominopelvic computed tomography (CT) with silver filter and deep learning reconstruction.
Scientific reports
Jul 16, 2025
To assess the image quality and radiation dose between reduced-dose CT with deep learning reconstruction (DLR) using SilverBeam filter and standard dose with iterative reconstruction (IR) in abdominopelvic CT. In total, 182 patients (mean age ± stand...
Non-invasive liver fibrosis screening on CT images using radiomics.
BMC medical imaging
Jul 15, 2025
PURPOSE: To develop a radiomics machine learning model for detecting liver fibrosis on CT images of the liver.
Establishing an AI-based diagnostic framework for pulmonary nodules in computed tomography.
BMC pulmonary medicine
Jul 12, 2025
BACKGROUND: Pulmonary nodules seen by computed tomography (CT) can be benign or malignant, and early detection is important for optimal management. The existing manual methods of identifying nodules have limitations, such as being time-consuming and ...
FF Swin-Unet: a strategy for automated segmentation and severity scoring of NAFLD.
BMC medical imaging
Jul 10, 2025
BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is a significant risk factor for liver cancer and cardiovascular diseases, imposing substantial social and economic burdens. Computed tomography (CT) scans are crucial for diagnosing NAFLD and ass...
Feasibility study of "double-low" scanning protocol combined with artificial intelligence iterative reconstruction algorithm for abdominal computed tomography enhancement in patients with obesity.
BMC medical imaging
Jul 9, 2025
OBJECTIVE: To evaluate the efficacy of the "double-low" scanning protocol combined with the artificial intelligence iterative reconstruction (AIIR) algorithm for abdominal computed tomography (CT) enhancement in obese patients and to identify the opt...
Robust Bi-CBMSegNet framework for advancing breast mass segmentation in mammography with a dual module encoder-decoder approach.
Scientific reports
Jul 8, 2025
Breast cancer is a prevalent disease affecting millions of women worldwide, and early screening can significantly reduce mortality rates. Mammograms are widely used for screening, but manual readings can lead to misdiagnosis. Computer-assisted diagno...
Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.
BMC medical imaging
Jul 2, 2025
To address misdiagnosis caused by feature coupling in multi-label medical image classification, this study introduces a chest X-ray pathology reasoning method. It combines hierarchical attention convolutional networks with a multi-label decoupling lo...
A deep learning-based computed tomography reading system for the diagnosis of lung cancer associated with cystic airspaces.
Scientific reports
Jul 2, 2025
To propose a deep learning model and explore its performance in the auxiliary diagnosis of lung cancer associated with cystic airspaces (LCCA) in computed tomography (CT) images. This study is a retrospective analysis that incorporated a total of 342...
Enhanced pulmonary nodule detection with U-Net, YOLOv8, and swin transformer.
BMC medical imaging
Jul 1, 2025
RATIONALE AND OBJECTIVES: Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for early pulmonary nodule detection to improve patient outcomes. Current methods encounter challenges in detecting s...
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