AI Medical Compendium Journal:
Radiology. Artificial intelligence

Showing 11 to 20 of 105 articles

Evaluation of Automated Public De-Identification Tools on a Corpus of Radiology Reports.

Radiology. Artificial intelligence
PURPOSE: To evaluate publicly available de-identification tools on a large corpus of narrative-text radiology reports.

Subspecialty-Level Deep Gray Matter Differential Diagnoses with Deep Learning and Bayesian Networks on Clinical Brain MRI: A Pilot Study.

Radiology. Artificial intelligence
PURPOSE: To develop and validate a system that could perform automated diagnosis of common and rare neurologic diseases involving deep gray matter on clinical brain MRI studies.

Erratum: Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

Radiology. Artificial intelligence
[This corrects the article DOI: 10.1148/ryai.2020190211.].

Generative Adversarial Networks Improve the Reproducibility and Discriminative Power of Radiomic Features.

Radiology. Artificial intelligence
PURPOSE: To assess the contribution of a generative adversarial network (GAN) to improve intermanufacturer reproducibility of radiomic features (RFs).

The Case for User-Centered Artificial Intelligence in Radiology.

Radiology. Artificial intelligence
Past technology transition successes and failures have demonstrated the importance of user-centered design and the science of human factors; these approaches will be critical to the success of artificial intelligence in radiology.

Construction of a Machine Learning Dataset through Collaboration: The RSNA 2019 Brain CT Hemorrhage Challenge.

Radiology. Artificial intelligence
This dataset is composed of annotations of the five hemorrhage subtypes (subarachnoid, intraventricular, subdural, epidural, and intraparenchymal hemorrhage) typically encountered at brain CT.

Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.

Radiology. Artificial intelligence
PURPOSE: To investigate improvements in performance for automatic bone age estimation that can be gained through model ensembling.