Author:
Gilany Mahdi,Wilson Paul,Jamzad Amoon,Fooladgar Fahimeh,To Minh Nguyen Nhat,Wodlinger Brian,Abolmaesumi Purang,Mousavi Parvin
Publisher
Springer Nature Switzerland
Reference21 articles.
1. Abdar, M., et al.: A review of uncertainty quantification in deep learning: techniques, applications and challenges. Inf. Fusion 76, 243–297 (2021)
2. Abouassaly, R., Klein, E.A., El-Shefai, A., Stephenson, A.: Impact of using 29 mhz high-resolution micro-ultrasound in real-time targeting of transrectal prostate biopsies: initial experience. World J. Urol. 38(5), 1201–1206 (2020)
3. Ahmed, H.U., et al.: Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study. Lancet 389(10071), 815–822 (2017)
4. Eure, G., Fanney, D., Lin, J., Wodlinger, B., Ghai, S.: Comparison of conventional transrectal ultrasound, magnetic resonance imaging, and micro-ultrasound for visualizing prostate cancer in an active surveillance population: a feasibility study. Can. Urol. Assoc. J. 13(3), E70 (2019)
5. Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050–1059. PMLR (2016)
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献