AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.
Medical image analysis
Apr 19, 2025
Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical settings, the segmentation of lung infections from computed tomography images enables rapid and accu...
World of Forms: Deformable geometric templates for one-shot surface meshing in coronary CT angiography.
Medical image analysis
Apr 18, 2025
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge. Th...
A comparison of an integrated and image-only deep learning model for predicting the disappearance of indeterminate pulmonary nodules.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 11, 2025
BACKGROUND: Indeterminate pulmonary nodules (IPNs) require follow-up CT to assess potential growth; however, benign nodules may disappear. Accurately predicting whether IPNs will resolve is a challenge for radiologists. Therefore, we aim to utilize d...
Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...
Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.
BMC medical imaging
Apr 10, 2025
OBJECTIVE: Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
Head
Aged, 80 and over
Signal-To-Noise Ratio
Adult
Computed Tomography Angiography
Tomography, X-Ray Computed
Middle Aged
Head and Neck Neoplasms
Male
Radiography, Dual-Energy Scanned Projection
Radiographic Image Interpretation, Computer-Assisted
Female
Neck
Deep Learning
Humans
Quality Improvement
Aged
Algorithms
Artifacts
Prospective Studies
Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses.
Japanese journal of radiology
Apr 7, 2025
PURPOSE: To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols.
Coronary p-Graph: Automatic classification and localization of coronary artery stenosis from Cardiac CTA using DSA-based annotations.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 4, 2025
Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implications. Digital subtraction angiography (DSA) remains the gold standard for diagnosing vascular disease, but its invasiveness and procedural demands unde...
Deep learning prediction of mammographic breast density using screening data.
Scientific reports
Apr 4, 2025
This study investigated a series of deep learning (DL) models for the objective assessment of four categories of mammographic breast density (e.g., fatty, scattered, heterogeneously dense, and extremely dense). A retrospective analysis was conducted ...
Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation.
IEEE transactions on medical imaging
Apr 3, 2025
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopulmonary radiological findings in CXRs, while the latter summarizes the ...
Improved unsupervised 3D lung lesion detection and localization by fusing global and local features: Validation in 3D low-dose computed tomography.
Medical image analysis
Apr 1, 2025
Unsupervised anomaly detection (UAD) is crucial in low-dose computed tomography (LDCT). Recent AI technologies, leveraging global features, have enabled effective UAD with minimal training data of normal patients. However, this approach, devoid of ut...
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