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2018 ASM / R-0041
Wiley-Blackwell Best Exhibit Award, Radiology
A six-year Australian and New Zealand experience of artificial intelligence techniques applied to MRI liver data – tricks and traps
Congress: 2018 ASM
Poster No.: R-0041
Type: Educational Exhibit
Keywords: Computer Applications-Detection, diagnosis, Neural networks, Computer applications, Image verification
Authors: M. Blake1, T. Cooper2, S. Khorshid1, T. St Pierre1; 1WA/AU, 2NZ
DOI:10.1594/ranzcr2018/R-0041

References

 

 

  1. Brownlee J. Supervised and Unsupervised Machine Learning Algorithms, https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
  2. House M.J., Bangma S.J., Thomas M., Gan E.K., Ayonrinde O.T., Adams L.A., Olynyk J.K., St. Pierre T.G. (2015) Texture-based classification of liver fibrosis using MRI. Journal of Magnetic Resonance Imaging 41: 322-328.  
  3. St Pierre T.G., House M.J., Mian A., Bangma S., Burgess G., Standish R.A., Casey S., Hornsey E., Angus P.W. (2015) Machine-learned Image Analysis Models for Classifying Liver Fibrosis Stage from Magnetic Resonance Images. Hepatology 62: 607A.
  4. Pan S.J. and Yanq Q., "A Survey on Transfer Learning," in IEEE Transactions on Knowledge and Data Engineering", vol.22, no.10,pp.1345-1359, Oct.2010
  5. Schmidhuber, J. "Deep learning in neural networks: An overview." Neural Networks 61 (2015): 85-117.
  6. Friedman, J. H. "Greedy Function Approximation: A Gradient Boosting Machine." The Annals of Statistics, vol. 29, no. 5, 2001, pp. 1189–1232.
  7. Parkhi O.M., Vedaldi A., Zisserman A., "Deep Face Recognition" British Machine Vision Conference, 2015.
  8. St Pierre T., Cooper T., Trang N.N., Ha N.T.T., Ton D.T., Viet N.K., Thong P.M., Tri N.A., Khanh B.Q., Boulos S., House M., Pang W., Bangma S., Taher A. (2017) Measurement of Liver Iron Concentration in a Population of Non-Transfusion Dependent Thalassemia Patients Using a Trained Artificial Neural Network to Analyse Magnetic Resonance Images. Blood 130: 2212.
  9. "MatConvNet - Convolutional Neural Networks for MATLAB", A. Vedaldi and K. Lenc, Proc. of the ACM Int. Conf. on Multimedia, 2015.
  10. Chen, Tianqi, Tong He, and Michael Benesty. "Xgboost: extreme gradient boosting." R package version 0.4-2 (2015): 1-4.

 

Acknowledgements: Alison Laws, CEO, Resonance Health Ltd; Ajmal Mian, The University of Western Australia; Sander Bangma, Moodle; Wenjie Pang, Resonance Health Ltd; Mike House, Resonance Health Ltd.

 

 

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