Diagnostic Radiology

Current Applications, Opportunities, and Limitations of AI for 3D Imaging in Dental Research and Practice.

The increasing use of three-dimensional (3D) imaging techniques in dental medicine has boosted the d...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several imp...

Concept attribution: Explaining CNN decisions to physicians.

Deep learning explainability is often reached by gradient-based approaches that attribute the networ...

Artificial intelligence and radiomics in pediatric molecular imaging.

In the past decade, a new approach for quantitative analysis of medical images and prognostic modell...

Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework.

The advancement of artificial intelligence concurrent with the development of medical imaging techni...

Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (Review).

The new era of artificial intelligence (AI) has introduced revolutionary data‑driven analysis paradi...

Medical Imaging and Privacy in the Era of Artificial Intelligence: Myth, Fallacy, and the Future.

Technological advances for the detection of melanoma: Advances in diagnostic techniques.

Managing the balance between accurately identifying early stage melanomas while avoiding obtaining b...

Understanding artificial intelligence based radiology studies: What is overfitting?

Artificial intelligence (AI) is a broad umbrella term used to encompass a wide variety of subfields ...

Machine learning volumetry of ischemic brain lesions on CT after thrombectomy-prospective diagnostic accuracy study in ischemic stroke patients.

PURPOSE: Ischemic lesion volume (ILV) is an important radiological predictor of functional outcome i...

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers.

PRIMAGE is one of the largest and more ambitious research projects dealing with medical imaging, art...

Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review.

There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning and...

Augmented patient-specific functional medical imaging by implicit manifold learning.

This paper uses machine learning to enrich magnetic resonance angiography and magnetic resonance ima...

Artificial intelligence, diagnostic imaging and neglected tropical diseases: ethical implications.

Towards data-driven medical imaging using natural language processing in patients with suspected urolithiasis.

OBJECTIVE: The majority of radiological reports are still written as free text and lack structure. F...

Radiomics: from qualitative to quantitative imaging.

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult ...

ChronoMID-Cross-modal neural networks for 3-D temporal medical imaging data.

ChronoMID-neural networks for temporally-varying, hence Chrono, Medical Imaging Data-makes the novel...

Preparing Medical Imaging Data for Machine Learning.

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The potent...

Integrating artificial intelligence into the clinical practice of radiology: challenges and recommendations.

Artificial intelligence (AI) has the potential to significantly disrupt the way radiology will be pr...

Unsupervised Domain Adaptation to Classify Medical Images Using Zero-Bias Convolutional Auto-Encoders and Context-Based Feature Augmentation.

The accuracy and robustness of image classification with supervised deep learning are dependent on t...

Real-world applications of deep convolutional neural networks in diagnostic cancer imaging.