AIMC Topic: Radiography

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Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI.

Journal of magnetic resonance imaging : JMRI
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep-learning algorithms have shown groundbreaking performance in a variety ...

The RSNA Pediatric Bone Age Machine Learning Challenge.

Radiology
Purpose The Radiological Society of North America (RSNA) Pediatric Bone Age Machine Learning Challenge was created to show an application of machine learning (ML) and artificial intelligence (AI) in medical imaging, promote collaboration to catalyze ...

Refining Convolutional Neural Network Detection of Small-Bowel Obstruction in Conventional Radiography.

AJR. American journal of roentgenology
OBJECTIVE: The purpose of this study was to evaluate improvement of convolutional neural network detection of high-grade small-bowel obstruction on conventional radiographs with increased training set size.

A dataset of clinically generated visual questions and answers about radiology images.

Scientific data
Radiology images are an essential part of clinical decision making and population screening, e.g., for cancer. Automated systems could help clinicians cope with large amounts of images by answering questions about the image contents. An emerging area...

Ossification area localization in pediatric hand radiographs using deep neural networks for object detection.

PloS one
BACKGROUND: Detection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but thei...

Annotation of enhanced radiographs for medical image retrieval with deep convolutional neural networks.

PloS one
The number of images taken per patient scan has rapidly increased due to advances in software, hardware and digital imaging in the medical domain. There is the need for medical image annotation systems that are accurate as manual annotation is imprac...

Forensic age estimation for pelvic X-ray images using deep learning.

European radiology
PURPOSE: To develop a deep learning bone age assessment model based on pelvic radiographs for forensic age estimation and compare its performance to that of the existing cubic regression model.

Translation of morphological and functional musculoskeletal imaging.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society
In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring, and assessing the extent of whole joint degeneration, the past decade has been marked by a greatly increased role...