Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.

@article{Yasaka2017DeepLW,
  title={Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.},
  author={Koichiro Yasaka and Hiroyuki Akai and Osamu Abe and Shigeru Kiryu},
  journal={Radiology},
  year={2017},
  volume={286 3},
  pages={
          887-896
        }
}
Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B… CONTINUE READING
BETA
Tweets
This paper has been referenced on Twitter 12 times. VIEW TWEETS

Citations

Publications citing this paper.
SHOWING 1-10 OF 17 CITATIONS

References

Publications referenced by this paper.
SHOWING 1-10 OF 21 REFERENCES

Investigation of the freely available easy - to - use software ‘ EZR ’ for medical statistics

  • C Szegedy, W Liu, Y Jia, Y Kanda
  • Bone Marrow Transplant
  • 2013

Similar Papers

Loading similar papers…