Radiology

Latest AI and machine learning research in radiology for healthcare professionals.

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You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.

Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishe...

Leveraging paired mammogram views with deep learning for comprehensive breast cancer detection.

Employing two standard mammography views is crucial for radiologists, providing comprehensive insigh...

Age-stratified deep learning model for thyroid tumor classification: a multicenter diagnostic study.

OBJECTIVES: Thyroid cancer, the only cancer that uses age as a specific predictor of survival, is in...

Personalized auto-segmentation for magnetic resonance imaging-guided adaptive radiotherapy of large brain metastases.

BACKGROUND AND PURPOSE: Magnetic resonance-guided adaptive radiotherapy (MRgART) may improve the eff...

[Artificial intelligence in radiology : Literature overview and reading recommendations].

BACKGROUND: Due to the ongoing rapid advancement of artificial intelligence (AI), including large la...

Synthetic CT generation from CBCT and MRI using StarGAN in the Pelvic Region.

RATIONALE AND OBJECTIVES: This study evaluated StarGAN, a deep learning model designed to generate s...

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network.

Accurate and precise identification of cholelithiasis is essential for saving the lives of millions ...

ThyroNet-X4 genesis: an advanced deep learning model for auxiliary diagnosis of thyroid nodules' malignancy.

Thyroid nodules are a common endocrine condition, and accurate differentiation between benign and ma...

Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.

In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and ma...

Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning.

To develop a deep learning model using transfer learning for automatic detection and segmentation of...

Integrating Eye Tracking With Grouped Fusion Networks for Semantic Segmentation on Mammogram Images.

Medical image segmentation has seen great progress in recent years, largely due to the development o...

IPNet: An Interpretable Network With Progressive Loss for Whole-Stage Colorectal Disease Diagnosis.

Colorectal cancer plays a dominant role in cancer-related deaths, primarily due to the absence of ob...

M₂DC: A Meta-Learning Framework for Generalizable Diagnostic Classification of Major Depressive Disorder.

Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The...

Radiomics Analysis of Different Machine Learning Models based on Multiparametric MRI to Identify Benign and Malignant Testicular Lesions.

RATIONALE AND OBJECTIVES: To develop and validate a machine learning-based prediction model for the ...

Prenatal Diagnostics Using Deep Learning: A Dual Approach to Plane Localization and Cerebellum Segmentation in Ultrasound Images.

OBJECTIVE: The fetal ultrasound examination is the significant task of mid-term pregnancy inspection...

Radiomics in glioma: emerging trends and challenges.

Radiomics is a promising neuroimaging technique for extracting and analyzing quantitative glioma fea...

Feasibility of remote robot empowered teleultrasound scanning for radioactive patients.

To investigate the feasibility of robot-assisted teleultrasound diagnosis for radioactive patients c...

Harmonizing flows: Leveraging normalizing flows for unsupervised and source-free MRI harmonization.

Lack of standardization and various intrinsic parameters for magnetic resonance (MR) image acquisiti...

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