Diagnostic Radiology

NiftyNet: a deep-learning platform for medical imaging.

BACKGROUND AND OBJECTIVES: Medical image analysis and computer-assisted intervention problems are in...

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...

Computational Intelligence for Medical Imaging Simulations.

This paper describes how to simulate medical imaging by computational intelligence to explore areas ...

Deep Learning in Nuclear Medicine and Molecular Imaging: Current Perspectives and Future Directions.

Recent advances in deep learning have impacted various scientific and industrial fields. Due to the ...

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 applicat...

Sensor, Signal, and Imaging Informatics.

To summarize significant contributions to sensor, signal, and imaging informatics published in 2016...

Deep learning poised to revolutionise diagnostic imaging.

Overview of deep learning in medical imaging.

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including...

Medical image classification via multiscale representation learning.

Multiscale structure is an essential attribute of natural images. Similarly, there exist scaling phe...

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

With the rapid development of modern medical imaging technology, medical image classification has be...

Deep Learning in Medical Imaging: General Overview.

The artificial neural network (ANN)-a machine learning technique inspired by the human neuronal syna...

Precision Radiology: Predicting longevity using feature engineering and deep learning methods in a radiomics framework.

Precision medicine approaches rely on obtaining precise knowledge of the true state of health of an ...

Machine Learning in Multimodal Medical Imaging.

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...

Machine Learning for Medical Imaging.

Machine learning is a technique for recognizing patterns that can be applied to medical images. Alth...

Multimodal Imaging in Diabetic Macular Edema.

Throughout ophthalmic history it has been shown that progress has gone hand in hand with technologic...

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 m...

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...

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 l...

Automated Outcome Classification of Computed Tomography Imaging Reports for Pediatric Traumatic Brain Injury.

BACKGROUND: The authors have previously demonstrated highly reliable automated classification of fre...

Predicting High Imaging Utilization Based on Initial Radiology Reports: A Feasibility Study of Machine Learning.

RATIONALE AND OBJECTIVES: Imaging utilization has significantly increased over the last two decades,...