Radiology

Multitask Deep Learning-Based Whole-Process System for Automatic Diagnosis of Breast Lesions and Axillary Lymph Node Metastasis Discrimination from Dynamic Contrast-Enhanced-MRI: A Multicenter Study.

BACKGROUND: Accurate diagnosis of breast lesions and discrimination of axillary lymph node (ALN) met...

Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.

OBJECTIVE: To compare the image quality and diagnostic performance between standard turbo spin-echo ...

Editorial Comment: Hepatic Steatosis-Contrast-Enhanced CT Is a Leading Mark.

Hardware deployment of deep learning model for classification of breast carcinoma from digital mammogram images.

Cancer is an illness that instils fear in many individuals throughout the world due to its lethal na...

Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.

PURPOSE: In Dynamic contrast-enhanced MRI (DCE-MRI), Arterial Input Function (AIF) has been shown to...

Shortening Acquisition Time and Improving Image Quality for Pelvic MRI Using Deep Learning Reconstruction for Diffusion-Weighted Imaging at 1.5 T.

RATIONALE AND OBJECTIVES: To determine the impact on acquisition time reduction and image quality of...

Investigating the impact of cognitive biases in radiologists' image interpretation: A scoping review.

RATIONALE AND OBJECTIVE: Image interpretation is a fundamental aspect of radiology. The treatment an...

Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

The security of AI systems has gained significant attention in recent years, particularly in the med...

Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup.

In this white paper, the ACR Pediatric AI Workgroup of the Commission on Informatics educates the ra...

Application of Semi-supervised Fuzzy Clustering Based on Knowledge Weighting and Cluster Center Learning to Mammary Molybdenum Target Image Segmentation.

Breast cancer is commonly diagnosed with mammography. Using image segmentation algorithms to separat...

Automatic Myocardial Contrast Echocardiography Image Quality Assessment Using Deep Learning: Impact on Myocardial Perfusion Evaluation.

OBJECTIVE: The image quality of myocardial contrast echocardiography (MCE) is critical for precise m...

Generative Adversarial Networks in Medicine: Important Considerations for this Emerging Innovation in Artificial Intelligence.

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of...

Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism.

INTRODUCTION: The objective was to perform placental ultrasound image texture (UPIA) in first (T1), ...

ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI.

Deep artificial neural networks (DNNs) have moved to the forefront of medical image analysis due to ...

Deep learning-based motion correction algorithm for coronary CT angiography: Lowering the phase requirement for morphological and functional evaluation.

PURPOSE: To investigate the performance of a deep learning-based motion correction algorithm (MCA) a...

Unsupervised deep learning-based displacement estimation for vascular elasticity imaging applications.

. Arterial wall stiffness can provide valuable information on the proper function of the cardiovascu...

Deep Learning-Based Motion Correction in Projection Domain for Coronary Computed Tomography Angiography: A Clinical Evaluation.

OBJECTIVE: This study aimed to evaluate the clinical performance of a deep learning-based motion cor...

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma.

BACKGROUND: Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in...

A CT-based Deep Learning Radiomics Nomogram for the Prediction of EGFR Mutation Status in Head and Neck Squamous Cell Carcinoma.

RATIONALE AND OBJECTIVES: Accurately assessing epidermal growth factor receptor (EGFR) mutation stat...

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lympho...

Deep Learning for Inference of Hepatic Proton Density Fat Fraction From T1-Weighted In-Phase and Opposed-Phase MRI: Retrospective Analysis of Population-Based Trial Data.

The confounder-corrected chemical shift-encoded MRI (CSE-MRI) sequence used to determine proton den...