Deep Learning: An Update for Radiologists

Preparing Medical Imaging Data for Machine Learning

(2020) Martin J. Willemink et al.   RADIOLOGY

Continuous Learning AI in Radiology: Implementation Principles and Early Applications

(2020) Oleg S. Pianykh et al.   RADIOLOGY

Tackling the Radiological Society of North America Pneumonia Detection Challenge

(2019) Ian Pan et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks

(2019) Veit Sandfort et al.   Scientific Reports

Ethics of Artificial Intelligence in Radiology: Summary of the Joint European and North American Multisociety Statement

(2019) J. Raymond Geis et al.   Journal of the American College of Radiology

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

(2018) Liang-Chieh Chen et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

The relative performance of ensemble methods with deep convolutional neural networks for image classification

(2018) Cheng Ju et al.   JOURNAL OF APPLIED STATISTICS

Natural Language–based Machine Learning Models for the Annotation of Clinical Radiology Reports

(2018) John Zech et al.   RADIOLOGY

Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction

(2018) Seong Ho Park et al.   RADIOLOGY

The RSNA Pediatric Bone Age Machine Learning Challenge

(2018) Safwan S. Halabi et al.   RADIOLOGY

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

(2017) Vijay Badrinarayanan et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Deep Learning: A Primer for Radiologists

(2017) Gabriel Chartrand et al.   RADIOGRAPHICS

Deep learning

(2015) Yann LeCun et al.   NATURE

The Pascal Visual Object Classes (VOC) Challenge

(2009) Mark Everingham et al.   INTERNATIONAL JOURNAL OF COMPUTER VISION