未來展望

本研究所提出的 OCT2HE 模型，利用非成對影像所做的訓練，還有一些可 嘗試的部分或許能夠使影像轉換的結果更好。第一，OCT2HE 模型使用 ResNet 的架構，擁有9 個短跳躍連接，然而並沒有嘗試將長跳躍連接也一起放入 ResNet

參考文獻

[1] 陳昱彤, “全域式光學同調斷層掃描術用於動物眼睛模型之特性分析,” 國立 台灣大學光電工程學研究所, 2018.

[2] J. K. C. Chan, “The Wonderful Colors of the Hematoxylin–Eosin Stain in Diagnostic Surgical Pathology,” International Journal of Surgical Pathology, vol.

22, issue 1, pp. 12-32, 2014.

[3] I. J. Goodfellow et al.,” Generative adversarial nets,” NIPS (International Conference on Neural Information Processing Systems), vol. 2, pp. 2672-2680 [4] Y. Rivenson, et al. “Virtual histological staining of unlabelled tissue

autofluorescence images via deep learning,” Nature Biomedical Engineering, vol.

3, pp. 466–477, 2019.

[5] X. Chapeleau et al, Measurements using Optic and RF Waves. New Jersey: John Wiley & Sons, Inc., 2013.

[6] 游鈐, “Mirau 全域式光學同調斷層掃描術結合近紅外光拉曼光譜用於皮膚細 胞之影像與頻譜特性分析,” 國立台灣大學光電工程學研究所, 2018.

[7] W. Drexler and J. G. Fujimoto, Optical Coherence Tomography – technologies and applications. Second edition. Springer, 2008

[8] C. Burrus and J. Stone, “Single-crystal fiber optical devices: A Nd: YAG fiber laser,” Applied Physics Letters, vol. 26, pp. 318-320, 1975.

[9] “Optical constants of Fused silica.” [Online]. Available:

https://refractiveindex.info/?shelf=glass&book=fused_silica&page=Malitson [10] 林彥宏, “Mirau 全域式光學同調斷層掃描於皮膚結構及動態特性分析,” 國

立台灣大學光電工程學研究所, 2016.

[11] “The Rayleigh Criterion.” [Online]. Available:

http://hyperphysics.phy-astr.gsu.edu/hbase/phyopt/Raylei.html

[12] “Camera Resolution.” [Online]. Available:

https://www.photonics.com/a29926/Camera_Resolution_Combining_Detector_a nd_Optics

[13] “皮膚生理學.” [Online]. Available:

https://www.e-champ.com.tw/skin.php [14] “Skin structure” [Online]. Available:

https://lh3.googleusercontent.com/i6W5QmVFDaNFfRE9uyYXUucW34D4ErU Wdi-YMFkMdMxo6RjHnPk560Pb22xalY7g4sHaGwYB5__aHDAQk767TIr8 WSPeK8lGLeElKT3eloiNIKEu3AkHAYuSuiZMKMi6qbMvyPzS

[15] “皮膚的生理結構.” [Online]. Available:

http://drtonywu.pixnet.net/blog/post/26010100-【醫師專欄-皮膚】-認識皮膚的 生理結構-struct.

[16] “Layers of epidermis.” [Online]. Available:

https://www.earthslab.com/physiology/cells-layers-epidermis/

[17] “石蠟切片.” [Online]. Available:

https://www.itsfun.com.tw/%E7%9F%B3%E8%A0%9F%E5%88%87%E7%89

%87/wiki-8805363

[18] “冰凍切片.” [Online]. Available:

https://www.itsfun.com.tw/%E5%86%B0%E5%87%8D%E5%88%87%E7%89

%87/wiki-3366017-7019886 [19] “黑色素細胞.” [Online]. Available:

https://smallcollation.blogspot.com/2013/09/melanocyte.html#gsc.tab=0 [20] “Langerhans cells.” [Online]. Available:

https://basicmedicalkey.com/skin-5/

[21] “Lentiginous nevus. Melanocytes (grey, not pigmented) vs basal keratinocytes

(brown melanin).” [Online]. Available:

https://twitter.com/JMGardnerMD/status/883502151393300483

[22] D. C. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber,” Deep neural networks segment neuronal membranes in electron microscopy images,” NIPS (International Conference on Neural Information Processing Systems), vol. 2, pp.

2843-2851, 2012.

[23] E. Shelhamer, J. Long and T. Darrell,” Fully Convolutional Networks for Semantic Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, issue 4, pp. 640-651, 2017.

[24] O. Ronneberger, P. Fischer and T. Brox,” U-Net: Convolutional Networks for Biomedical Image Segmentation,” MICCAI (Medical Image Computing and Computer-Assisted Intervention), vol. 9351, pp. 234-241, 2015.

[25] K. Y. T. Seet, T. A. Nieminen and A. Zyvagin,” Refractometry of melanocyte cell nuclei using optical scatter images recorded by digital Fourier microscopy,”

Journal of Biomedical Optics, vol. 14, issue 4, 2009.

[26] “Hung-yi Lee’s Machine learning courses, Conditional GAN, spring, 2018”

[Online]. Available:

http://speech.ee.ntu.edu.tw/~tlkagk/courses/MLDS_2018/Lecture/CGAN.pdf [27] “Hung-yi Lee’s Machine learning courses, “Unsupervised Learning: Deep

Auto-encoder”, fall, 2017” [Online]. Available:

http://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2017/Lecture/auto.pdf

[28] “Hung-yi Lee’s Machine learning courses, “GAN Theory”, spring, 2018” [Online].

Available:

http://speech.ee.ntu.edu.tw/~tlkagk/courses/MLDS_2018/Lecture/GANtheory%2 0(v2).pdf

[29] “熵(資訊理論).” [Online]. Available:

https://zh.wikipedia.org/wiki/%E7%86%B5_(%E4%BF%A1%E6%81%AF%E8

%AE%BA)

[30] S. Nowozin, B. Cseke and R. Tomioka,” f-GAN: training generative neural samplers using variational divergence minimization,” NIPS (International Conference on Neural Information Processing Systems), pp. 271-279, 2016.

[31] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” ICCV (IEEE International Conference on Computer Vision), pp.2242-2251, 2017

[32] M.-Y. Liu, T. Breuel, and J. Kautz, “Unsupervised image-to-image translation networks,” NIPS (International Conference on Neural Information Processing Systems), 2017.

[33] H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang, “Diverse image-to-image translation via disentangled representations,” ECCV (European Conference on Computer Vision), 2018.

[34] A. Radford, L. Metz and S. Chintala,” Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks,” ICLR (International Conference on Learning Representations), 2016.

[35] M. Mirza and S. Osindero, “Conditional generative adversarial nets”, 2014.

[36] “What should I do if the synthetic image in target is similar the real image in source?” [Online]. Available:

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/618 [37] “Unet or Resnet, which one is better for pix2pix model?” [Online]. Available:

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/117

[38] P. Isola, J. Zhu, T. Zhou and A. A. Efros, “Image-to-Image Translation with

Conditional Adversarial Networks.” CVPR (IEEE Conference on Computer Vision and Pattern Recognition), pp. 5967-5976, 2017.

[39] J. Johnson, A. Alahi and F.-F. Lee, “Perceptual Losses for Real-Time Style Transfer and Super-Resolution”, ECCV (European Conference on Computer Vision), 2016.

[40] “Sigmoid function.” [Online]. Available:

https://stackoverflow.com/questions/49977063/role-derivative-of-sigmoid-function-in-neural-networks

[41] X. Mao et al., ” Least Squares Generative Adversarial Networks.” ICCV (IEEE International Conference on Computer Vision), pp. 2813-2821, 2017.

[42] “CycleGAN loss curve.” [Online]. Available:

https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/issues/30

[43] S. Liu and W. Deng, “Very deep convolutional neural network based image classification using small training sample size,” ACPR (Asian Conference on Pattern Recognition), 3rd IAPR Asian Conference, pp. 730-734, 2015.

[44] “ImageNet” [Online]. Available:

http://www.image-net.org/

[45] “ImageNet: what is top-1 and top-5 error rate?” [Online]. Available:

https://stats.stackexchange.com/questions/156471/imagenet-what-is-top-1-and-top-5-error-rate

[46] M. Drozdzal, E. Vorontsov, G. Chartrand, S. Kadoury and C. Pal, “The Importance of Skip Connections in Biomedical Image Segmentation,” DLMIA (Workshop on Deep Learning in Medical Image Analysis), 2016.

附錄 1 本論文中所使用之影像分割與影像轉換

並使用Adam 作為優化器(Optimizer)來更新參數，其式子如下所示。

 