AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Phantom-based evaluation of image quality in Transformer-enhanced 2048-matrix CT imaging at low and ultralow doses.

Japanese journal of radiology
PURPOSE: To compare the quality of standard 512-matrix, standard 1024-matrix, and Swin2SR-based 2048-matrix phantom images under different scanning protocols.

Kidney cancer diagnosis and surgery selection by double decker convolutional neural network from CT scans combined with great wall construction algorithm.

Abdominal radiology (New York)
One of the most prevalent cancers in the world is kidney cancer (KC). A precise diagnosis, which is influenced by a number of variables, such as the size or volume of the tumor, the types and stages of the cancer, etc., is essential for the treatment...

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
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
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 ...

Arterial phase CT radiomics for non-invasive prediction of Ki-67 proliferation index in pancreatic solid pseudopapillary neoplasms.

Abdominal radiology (New York)
BACKGROUND: This study aimed to preoperatively predict Ki-67 proliferation levels in patients with pancreatic solid pseudopapillary neoplasm (pSPN) using radiomics features extracted from arterial phase helical CT images.

Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation.

IEEE transactions on medical imaging
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
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...

Semi-supervised spatial-frequency transformer for metal artifact reduction in maxillofacial CT and evaluation with intraoral scan.

European journal of radiology
PURPOSE: To develop a semi-supervised domain adaptation technique for metal artifact reduction with a spatial-frequency transformer (SFTrans) model (Semi-SFTrans), and to quantitatively compare its performance with supervised models (Sup-SFTrans and ...