Deep learning models for triaging hospital head MRI examinations

Author:

Wood David A.,Kafiabadi Sina,Busaidi Ayisha Al,Guilhem EmilyORCID,Montvila AntanasORCID,Lynch Jeremy,Townend MatthewORCID,Agarwal Siddharth,Mazumder AsifORCID,Barker Gareth J.ORCID,Ourselin Sebastien,Cole James H.ORCID,Booth Thomas C.

Funder

Royal College of Radiologists

King's College Hospital NHS Foundation Trust

Wellcome Trust

King's Health Partners

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Health Informatics,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology

Reference58 articles.

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2. Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018). Sanity checks for saliency maps. arXiv preprint arXiv:1810.03292.

3. American College of Radiology. ACR-ASNR-SPR practice parameter for the performance and interpretation of magnetic resonance imaging (MRI) of the brain. https://www.acr.org/-/media/ACR/Files/Practice-Parameters/MR-Brain.pdf. Published 2019. 2020-02-01.

4. Automated triaging of adult chest radiographs with deep artificial neural networks;Annarumma;Radiology,2019

5. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography;Ardila;Nat. Med.,2019

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