International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
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Volume 12 - Issue 13 |
Published: May 2018 |
Authors: Praneeth Sadda, Taha Qarni |
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Praneeth Sadda, Taha Qarni . Real-Time Medical Video Denoising with Deep Learning: Application to Angiography. International Journal of Applied Information Systems. 12, 13 (May 2018), 22-28. DOI=10.5120/ijais2018451755
@article{ 10.5120/ijais2018451755, author = { Praneeth Sadda,Taha Qarni }, title = { Real-Time Medical Video Denoising with Deep Learning: Application to Angiography }, journal = { International Journal of Applied Information Systems }, year = { 2018 }, volume = { 12 }, number = { 13 }, pages = { 22-28 }, doi = { 10.5120/ijais2018451755 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2018 %A Praneeth Sadda %A Taha Qarni %T Real-Time Medical Video Denoising with Deep Learning: Application to Angiography%T %J International Journal of Applied Information Systems %V 12 %N 13 %P 22-28 %R 10.5120/ijais2018451755 %I Foundation of Computer Science (FCS), NY, USA
This paper describes the design, training, and evaluation of a deep neural network for removing noise from medical fluoroscopy videos. The method described in this work, unlike the current standard techniques for video denoising, is able to deliver a result quickly enough to be used in real-time scenarios. Furthermore, this method is able to produce results of a similar quality to the existing industry-standard denoising techniques.