Diagnostic Radiology

Will machine learning end the viability of radiology as a thriving medical specialty?

There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in picture...

The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment.

Recently, researchers have built new deep learning (DL) models using a single image modality to diagnose age-related macular degeneration (AMD). Retinal fundus and optical coherence tomography (OCT) images in clinical settings are the most important modalities investigating AMD. Whether concomitant use of fundus and OCT data in DL technique is beneficial has not been so clearly identified. This ex...

Generative Adversarial Network for Medical Images (MI-GAN).

Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, ...

Health intelligence: how artificial intelligence transforms population and personalized health.

Advances in computational and data sciences for data management, integration, mining, classification, filtering, visualization along with engineering ...

Large-scale medical image annotation with crowd-powered algorithms.

Accurate segmentations in medical images are the foundations for various clinical applications. Advances in machine learning-based techniques show gre...

Automated deep-neural-network surveillance of cranial images for acute neurologic events.

Rapid diagnosis and treatment of acute neurological illnesses such as stroke, hemorrhage, and hydrocephalus are critical to achieving positive outcome...

Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology.

Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combinati...

Data Analysis Strategies in Medical Imaging.

Radiographic imaging continues to be one of the most effective and clinically useful tools within oncology. Sophistication of artificial intelligence ...

The future of radiology augmented with Artificial Intelligence: A strategy for success.

The rapid development of Artificial Intelligence/deep learning technology and its implementation into routine clinical imaging will cause a major tran...

Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success.

Worldwide interest in artificial intelligence (AI) applications, including imaging, is high and growing rapidly, fueled by availability of large datas...

Deep Learning in Radiology: Does One Size Fit All?

Deep learning (DL) is a popular method that is used to perform many important tasks in radiology and medical imaging. Some forms of DL are able to acc...

Multi-task transfer learning deep convolutional neural network: application to computer-aided diagnosis of breast cancer on mammograms.

Transfer learning in deep convolutional neural networks (DCNNs) is an important step in its application to medical imaging tasks. We propose a multi-t...

Overview of deep learning in medical imaging.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and me...

Medical image classification via multiscale representation learning.

Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phenomena in medical images, and therefore a wide ran...

Medical image classification based on multi-scale non-negative sparse coding.

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis ...

Creation of a simple natural language processing tool to support an imaging utilization quality dashboard.

BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of i...

Machine Learning for Medical Imaging.

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in render...

An Ensemble of Fine-Tuned Convolutional Neural Networks for Medical Image Classification.

The availability of medical imaging data from clinical archives, research literature, and clinical manuals, coupled with recent advances in computer v...

Transitive closure of subsumption and causal relations in a large ontology of radiological diagnosis.

The Radiology Gamuts Ontology (RGO)-an ontology of diseases, interventions, and imaging findings-was developed to aid in decision support, education, ...

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great d...