參考文獻 |
References
[1] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, “Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.,” CA. Cancer J. Clin., vol. 68, no. 6, pp. 394–424, 2018.
[2] M. S. Fuller, C. I. Lee, and J. G. Elmore, “Breast Cancer Screening : An Evidence-Based Update,” vol. 99, no. 3, pp. 451–468, 2016.
[3] A. Godavarty, S. Rodriguez, Y. J. Jung, and S. Gonzalez, “Optical imaging for breast cancer prescreening,” Breast Cancer Targets Ther., vol. 7, pp. 193–209, 2015.
[4] A. Gibson and H. Dehghani, “Diffuse optical imaging,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 367, no. 1900, pp. 3055–3072, Aug. 2009.
[5] J. C. Hebden, S. R. Arridge, and D. T. Delpy, “Optical imaging in medicine: I. Experimental techniques,” Phys. Med. Biol., vol. 42, no. 5, pp. 825–840, 1997.
[6] H. Dehghani, S. Sri Nivasan, B. W. Pogue, and A. Gibson, “Numerical modelling and image reconstruction in diffuse optical tomography,” Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., vol. 367, no. 1900, pp. 3073–3093, 2009.
[7] S. R. Arridge and J. C. Schotland, “Optical tomography: Forward and inverse problems,” Inverse Probl., vol. 25, no. 12, 2009.
[8] T. Durduran, R. Choe, W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Reports Prog. Phys., vol. 73, no. 7, p. 076701, Jul. 2010.
[9] B. W. Pogue, M. S. Patterson, H. Jiang, and K. D. Paulsen, “Initial assessment of a simple system for frequency domain diffuse optical tomography,” Phys. Med. Biol., vol. 40, no. 10, pp. 1709–1729, Oct. 1995.
[10] S. R. Arridge, M. Schweiger, M. Hiraoka, and D. T. Delpy, “A finite element approach for modeling photon transport in tissue,” Med. Phys., vol. 20, no. 2, pp. 299–309, Mar. 1993.
[11] K. D. Paulsen and H. Jiang, “Spatially varying optical property reconstruction using a finite element diffusion equation approximation,” Med. Phys., vol. 22, no. 6, pp. 691–701, 1995.
[12] M. A. O’Leary, D. A. Boas, B. Chance, and A. G. Yodh, “Experimental images of heterogeneous turbid media by frequency-domain diffusing-photon tomography,” Opt. Lett., vol. 20, no. 5, p. 426, 1995.
[13] M. A. O’Leary, “Imaging with diffuse photon density waves,” 1996.
[14] J. C. Hebden et al., “Three-dimensional time-resolved optical tomography of a conical breast phantom,” 2001.
[15] S. R. Arridge, “Optical tomography in medical imaging,” Inverse Probl., vol. 15, no. 2, pp. R41–R93, Apr. 1999.
[16] H. Dehghani et al., “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng., vol. 25, no. 6, pp. 711–732, Jun. 2009.
[17] N. Cao, A. Nehorai, and M. Jacobs, “Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm,” Opt. Express, vol. 15, no. 21, p. 13695, 2007.
[18] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Commun. ACM, vol. 60, no. 6, pp. 84–90, May 2017.
[19] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9351, 2015, pp. 234–241.
[20] D. M. Pelt and K. J. Batenburg, “Fast tomographic reconstruction from limited data using artificial neural networks,” IEEE Trans. Image Process., vol. 22, no. 12, pp. 5238–5251, 2013.
[21] S. Wang et al., “Accelerating magnetic resonance imaging via deep learning,” Proc. - Int. Symp. Biomed. Imaging, vol. 2016-June, pp. 514–517, 2016.
[22] B. Zhu, J. Z. Liu, S. F. Cauley, B. R. Rosen, and M. S. Rosen, “Image reconstruction by domain-transform manifold learning,” Nature, vol. 555, no. 7697, pp. 487–492, 2018.
[23] J. Yoo et al., “Deep Learning Can Reverse Photon Migration for Diffuse Optical Tomography,” vol. XXX, no. Xx, 2017.
[24] Y. Yao, Y. Wang, and R. L. Barbour, “of absorption and scattering distributions by a Born iterative method,” vol. 14, no. 1, pp. 325–342, 1997.
[25] H. Ben Yedder, A. BenTaieb, M. Shokoufi, A. Zahiremami, F. Golnaraghi, and G. Hamarneh, “Deep learning based image reconstruction for diffuse optical tomography,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 11074 LNCS, pp. 112–119, 2018.
[26] J. Feng, Q. Sun, Z. Li, Z. Sun, and K. Jia, “Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography,” J. Biomed. Opt., vol. 24, no. 05, p. 1, 2018.
[27] M. T. McCann, K. H. Jin, and M. Unser, “Convolutional neural networks for inverse problems in imaging: A review,” IEEE Signal Process. Mag., vol. 34, no. 6, pp. 85–95, 2017.
[28] A. Lucas, M. Iliadis, R. Molina, and A. K. Katsaggelos, “Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods,” IEEE Signal Process. Mag., vol. 35, no. 1, pp. 20–36, 2018.
[29] K. He, “Deep Residual Learning for Image Recognition.”
[30] S. R. Arridge and M. Schweiger, “Part 2 : Finite-element-method calculations e e e e,” Appl. Opt., vol. 34, no. 34, pp. 8026–37, 1995.
[31] T. J. Farrell, M. S. Patterson, and B. Wilson, “A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo,” Med. Phys., vol. 19, no. 4, pp. 879–888, Jul. 1992.
[32] W. Egan, Optical properties of inhomogeneous materials: Applications to geology, astronomy, chemistry, and engineering. London: Academic Press, 1979.
[33] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. Upper Saddle River, NJ: Pearson Education, 2010.
[34] I. J. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016.
[35] G. Yang et al., “DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction,” IEEE Trans. Med. Imaging, vol. 37, no. 6, pp. 1310–1321, 2018.
[36] S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” 32nd Int. Conf. Mach. Learn. ICML 2015, vol. 1, pp. 448–456, 2015.
[37] B. Xu, N. Wang, T. Chen, and M. Li, “Empirical Evaluation of Rectified Activations in Convolutional Network,” 2015.
[38] X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” J. Mach. Learn. Res., vol. 9, pp. 249–256, 2010.
[39] D. A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (ELUs),” 4th Int. Conf. Learn. Represent. ICLR 2016 - Conf. Track Proc., pp. 1–14, 2016.
[40] H. Zheng, Z. Yang, W. Liu, J. Liang, and Y. Li, “Improving deep neural networks using softplus units,” Proc. Int. Jt. Conf. Neural Networks, vol. 2015-Septe, no. July 2015, 2015.
[41] L.-Y. Chen, M.-C. Pan, C.-C. Yan, and M.-C. Pan, “Wavelength optimization using available laser diodes in spectral near-infrared optical tomography,” Appl. Opt., vol. 55, no. 21, p. 5729, 2016.
[42] L. Y. Chen, M. C. Pan, and M. C. Pan, “Visualized numerical assessment for near infrared diffuse optical tomography with contrast-and-size detail analysis,” Opt. Rev., vol. 20, no. 1, pp. 19–25, 2013.
[43] L. V. Wang and H. Wu, Biomedical Optics: Principles and Imaging. New Jersey: Wiley-Interscience, 2007. |