On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities-Reference-Cited by-同舟云学术

On the Interpretability of Artificial Intelligence in Radiology: Challenges and Opportunities

Published:2020-05-01 Issue:3 Volume:2 Page:e190043
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Reyes Mauricio^ORCID,Meier Raphael^ORCID,Pereira Sérgio^ORCID,Silva Carlos A.,Dahlweid Fried-Michael^ORCID,Tengg-Kobligk Hendrik von,Summers Ronald M.^ORCID,Wiest Roland

Funder

National Institutes of Health

Publisher

Radiological Society of North America (RSNA)

Subject

Artificial Intelligence,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2020190043

Reference32 articles.

1. High-performance medicine: the convergence of human and artificial intelligence

2. Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment

3. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

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