1 |
Kennedy P, Wagner M, Castera L, Hong CW, Johnson CL, Sirlin CB, Taouli B. Quantitative Elastography Methods in Liver Disease: Current Evidence and Future Directions. Radiology. 2018;286(3):738-63.
DOI
|
2 |
Lee JE, Shin KS, Cho JS, You SK, Min JH, Kim KH, Song IS, Cheon KS. Non-invasive Assessment of Liver Fibrosis with ElastPQ: Comparison with Transient Elastography and Serologic Fibrosis Marker Tests, and Correlation with Liver Pathology Results. Ultrasound Med Biol. 2017;43(11):2515-21.
DOI
|
3 |
Zhuang Y, Ding H, Zhang Y, Sun H, Xu C, Wang W. Twodimensional Shear-Wave Elastography Performance in the Noninvasive Evaluation of Liver Fibrosis in Patients with Chronic Hepatitis B: Comparison with Serum Fibrosis Indexes. Radiology. 2017; 283(3):873-82.
DOI
|
4 |
Sandrin L, Fourquet B, Hasquenoph JM, Yon S, Fournier C, Mal F, Christidis C, Ziol M, Poulet B, Kazemi F, Beaugrand M, Palau R. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis. Ultrasound Med Biol. 2003;29(12):1705-13.
DOI
|
5 |
Castera L, Vergniol J, Foucher J, Le Bail B, Chanteloup E, Haaser M, Darriet M, Couzigou P, De Ledinghen V. Prospec-tive comparison of transient elastography, Fibrotest, APRI and liver biopsy for the assessment of fibrosis in chronic hepatitis C. Gastroenterology. 2005;128(2):343-50.
DOI
|
6 |
Nightingale K, McAleavey S, Trahey G. Shear-wave generation using acoustic radiation force: in vivo and ex vivo results. Ultrasound in Medicine & Biology. 2003;29(12):1715-23.
DOI
|
7 |
Bercoff J, Tanter M, Fink M. Supersonic shear imaging: a new technique for soft tissue elasticity mapping. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2004;51(4):396-409.
DOI
|
8 |
Mo JA. Shear wave elastography: a systematic review and meta-analysis. Journal of the Korean Medical Association. 2016;59(7):529-35.
DOI
|
9 |
Meng D, Zhang L, Cao G, Cao W, Zhang G, Hu B. Liver Fibrosis Classification Based on Transfer Learning FCNet for Ultrasound Images. IEEE Access. 2017;5:5804-10.
DOI
|
10 |
Choi BH, Kim YJ, Choi SJ, Kim KG. Malignant and Benign Classification of Liver Tumor in CT according to Data pre-processing and Deep running model. J Biomed Eng Res. 2018; 39(6):229-36.
DOI
|
11 |
Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer. 2015;9351:234-41.
|
12 |
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. International Conference on Computer Vision (ICCV). 2017;618-26.
|
13 |
Zeiler MD. ADADELTA: An adaptive learning rate method. arXiv preprint 2012; arXiv:1212.5701.
|