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Radiology Artificial Intelligence
Scopus
ISSN 2638-6100 · Radiological Society of North America · 8 articles in the index
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Journal Article Full text
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update
To address the rapid evolution of artificial intelligence in medical imaging, the authors present the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) 2024 Update.
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Journal Article Full text
A Guide to Cross-Validation for Artificial Intelligence in Medical Imaging
Artificial intelligence (AI) is being increasingly used to automate and improve technologies within the field of medical imaging. A critical step in the development of an AI algorithm is estimating its prediction error through cross-validation (CV). The use of CV can help prevent overoptimism in AI…
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Journal Article
RadImageNet: An Open Radiologic Deep Learning Research Dataset for Effective Transfer Learning
Purpose To demonstrate the value of pretraining with millions of radiologic images compared with ImageNet photographic images on downstream medical applications when using transfer learning. Materials and Methods This retrospective study included patients who underwent a radiologic study between…
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Journal Article
Assessing the Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging
Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in medical imaging. Materials and Methods Using two large publicly available radiology datasets (Society for Imaging Informatics in Medicine–American College of Radiology Pneumothorax Segmentation dataset and…
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Journal Article
On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities
As artificial intelligence (AI) systems begin to make their way into clinical radiology practice, it is crucial to assure that they function correctly and that they gain the trust of experts. Toward this goal, approaches to make AI “interpretable” have gained attention to enhance the understanding…
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Journal Article
fastMRI: A Publicly Available Raw k-Space and DICOM Dataset of Knee Images for Accelerated MR Image Reconstruction Using Machine Learning
A publicly available dataset containing k-space data as well as Digital Imaging and Communications in Medicine image data of knee images for accelerated MR image reconstruction using machine learning is presented.
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Journal Article
The Effect of Image Resolution on Deep Learning in Radiography
Purpose To examine variations of convolutional neural network (CNN) performance for multiple chest radiograph diagnoses and image resolutions. Materials and Methods This retrospective study examined CNN performance using the publicly available National Institutes of Health chest radiograph dataset…