Diagnostic Radiology

Nearest Neighbor-Based Strategy to Optimize Multi-View Triplet Network for Classification of Small-Sample Medical Imaging Data.

Multi-view classification with limited sample size and data augmentation is a very common machine le...

Implementation of artificial intelligence in thoracic imaging-a what, how, and why guide from the European Society of Thoracic Imaging (ESTI).

This statement from the European Society of Thoracic imaging (ESTI) explains and summarises the esse...

Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging.

Non-rigid registration between 3D surfaces is an important but notorious problem in medical imaging,...

Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives.

Transformer, one of the latest technological advances of deep learning, has gained prevalence in nat...

Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging.

The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponentia...

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learni...

Ten quick tips for computational analysis of medical images.

Medical imaging is a great asset for modern medicine, since it allows physicians to spatially interr...

Virus Detection and Identification in Minutes Using Single-Particle Imaging and Deep Learning.

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the COVID...

Focus on machine learning models in medical imaging.

Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis.

BACKGROUND: Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection o...

Data augmentation for medical imaging: A systematic literature review.

Recent advances in Deep Learning have largely benefited from larger and more diverse training sets. ...

Telerobotic Sonography for Remote Diagnostic Imaging.

Artificial intelligence in diagnostic and interventional radiology: Where are we now?

The emergence of massively parallel yet affordable computing devices has been a game changer for res...

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta.

Digital imaging, including the use of artificial intelligence, has been increasingly applied to inve...

Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey.

Reinforcement learning takes sequential decision-making approaches by learning the policy through tr...

Mapping the Landscape of Care Providers' Quality Assurance Approaches for AI in Diagnostic Imaging.

The discussion on artificial intelligence (AI) solutions in diagnostic imaging has matured in recent...

NAVIGATOR: an Italian regional imaging biobank to promote precision medicine for oncologic patients.

NAVIGATOR is an Italian regional project boosting precision medicine in oncology with the aim of mak...

A Natural Language Processing and Machine Learning Approach to Identification of Incidental Radiology Findings in Trauma Patients Discharged from the Emergency Department.

STUDY OBJECTIVE: Patients undergoing diagnostic imaging studies in the emergency department (ED) com...

Value assessment of artificial intelligence in medical imaging: a scoping review.

BACKGROUND: Artificial intelligence (AI) is seen as one of the major disrupting forces in the future...

Trends in clinical validation and usage of US Food and Drug Administration-cleared artificial intelligence algorithms for medical imaging.

AIM: To examine the current landscape of US Food and Drug Administration (FDA)-approved artificial i...

Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging.

Thus far, there have been no reported specific rules for systematically determining the appropriate ...