研究生: |
張祐瑞 You-Rui Zhang |
---|---|
論文名稱: |
利用深度學習與合成資料實現胸部X光片之外來物與骨骼影像消除 Chest X-ray Artifact and Bone Shadow Reduction with Deep Learning and Synthesized Data |
指導教授: |
呂政修
Jenq-Shiou Leu |
口試委員: |
呂政修
蔡佳醍 力博宏 陳維美 |
學位類別: |
碩士 Master |
系所名稱: |
電資學院 - 電子工程系 Department of Electronic and Computer Engineering |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 中文 |
論文頁數: | 53 |
中文關鍵詞: | 深度學習 、雜訊消除模型 、胸部X光片 、外來物影像消除 、骨骼影像消除 、電腦輔助診斷系統 |
外文關鍵詞: | Deep Learning, De-noising Model, Chest X-Ray, Artifacts Reduction, Bone Shadow Reduction, Computer-Aided Diagnostic |
相關次數: | 點閱:178 下載:0 |
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[1] D. -P. Fan et al., "Inf-Net: Automatic COVID-19 Lung Infection Segmentation
From CT Images," in IEEE Transactions on Medical Imaging, vol. 39, no. 8, pp.
2626-2637, Aug. 2020, doi: 10.1109/TMI.2020.2996645.
[2] A. Waheed, M. Goyal, D. Gupta, A. Khanna, F. Al-Turjman and P. R. Pinheiro,
"CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved
Covid-19 Detection," in IEEE Access, vol. 8, pp. 91916-91923, 2020, doi:
10.1109/ACCESS.2020.2994762.
[3] X. Wang et al., "A Weakly-Supervised Framework for COVID-19 Classification
and Lesion Localization From Chest CT," in IEEE Transactions on Medical
Imaging, vol. 39, no. 8, pp. 2615-2625, Aug. 2020, doi:
10.1109/TMI.2020.2995965.
[4] S. Roy et al., "Deep Learning for Classification and Localization of COVID-19
Markers in Point-of-Care Lung Ultrasound," in IEEE Transactions on Medical
Imaging, vol. 39, no. 8, pp. 2676-2687, Aug. 2020, doi:
10.1109/TMI.2020.2994459.
[5] Simonyan, K., & Zisserman, A. (2015). Very Deep Convolutional Networks for
Large-Scale Image Recognition. CoRR, abs/1409.1556.
[6] Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. A. (2017, February). Inceptionv4, inception-resnet and the impact of residual connections on learning. In Thirtyfirst AAAI conference on artificial intelligence.
[7] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image
recognition. In Proceedings of the IEEE conference on computer vision and
pattern recognition (pp. 770-778).
[8] J. Deng, W. Dong, R. Socher, L. -J. Li, Kai Li and Li Fei-Fei, "ImageNet: A largescale hierarchical image database," 2009 IEEE Conference on Computer Vision
and Pattern Recognition, 2009, pp. 248-255, doi: 10.1109/CVPR.2009.5206848.
[9] Heidari, M., Mirniaharikandehei, S., Khuzani, A. Z., Danala, G., Qiu, Y., & Zheng,
B. (2020). Improving the performance of CNN to predict the likelihood of
COVID-19 using chest X-ray images with preprocessing algorithms. International
journal of medical informatics, 144, 104284.
[10] LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning
applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[11] Tian, C., Fei, L., Zheng, W., Xu, Y., Zuo, W., & Lin, C. W. (2020). Deep learning
on image denoising: An overview. Neural Networks, 131, 251-275.
[12] Majumdar, A. (2018). Blind denoising autoencoder. IEEE transactions on neural
44
networks and learning systems, 30(1), 312-317.
[13] Fonseca, A. U., Oliveira, L. L., Mombach, J., Fernandes, D. S., Salvini, R., &
Soares, F. (2020, August). Foreign artifacts detection on pediatric chest X-ray. In
2020 IEEE Canadian Conference on Electrical and Computer Engineering
(CCECE) (pp. 1-4). IEEE.
[14] Hogeweg, L., Sánchez, C. I., Melendez, J., Maduskar, P., Story, A., Hayward, A.,
& van Ginneken, B. (2013). Foreign object detection and removal to improve
automated analysis of chest radiographs. Medical physics, 40(7), 071901.
[15] Hogeweg, L., Sánchez, C. I., & van Ginneken, B. (2013). Suppression of
translucent elongated structures: applications in chest radiography. IEEE
transactions on medical imaging, 32(11), 2099-2113.
[16] Gusarev, M., Kuleev, R., Khan, A., Rivera, A. R., & Khattak, A. M. (2017, August).
Deep learning models for bone suppression in chest radiographs. In 2017 IEEE
Conference on Computational Intelligence in Bioinformatics and Computational
Biology (CIBCB) (pp. 1-7). IEEE.
[17] Rajaraman, S., Zamzmi, G., Folio, L., Alderson, P., & Antani, S. (2021). Chest xray bone suppression for improving classification of tuberculosis-consistent
findings. Diagnostics, 11(5), 840.
[18] Lin, C., Tang, R., Lin, D. D., Liu, L., Lu, J., Chen, Y., ... & Zhou, J. (2020, April).
Deep Feature Disentanglement Learning for Bone Suppression in Chest
Radiographs. In 2020 IEEE 17th International Symposium on Biomedical Imaging
(ISBI) (pp. 795-798). IEEE.
[19] Lee, D., Kim, H., Choi, B., & Kim, H. J. (2019). Development of a deep neural
network for generating synthetic dual-energy chest x-ray images with single x-ray
exposure. Physics in Medicine & Biology, 64(11), 115017.
[20] Kim, K., & Myung, H. (2018). Autoencoder-combined generative adversarial
networks for synthetic image data generation and detection of jellyfish swarm.
IEEE Access, 6, 54207-54214.
[21] Duffy, B. A., Toga, A. W., & Kim, H. (2020, April). Gradient Artifact Correction
for Simultaneous EEG-fMRI using Denoising Autoencoders. In 2020 IEEE 17th
International Symposium on Biomedical Imaging (ISBI) (pp. 1-4). IEEE.
[22] Adeel, H., Riaz, M. M., & Ali, S. S. (2022). De-Fencing and Multi-Focus Fusion
Using Markov Random Field and Image Inpainting. IEEE Access, 10, 35992-
36005.
[23] Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017). Beyond a gaussian
denoiser: Residual learning of deep cnn for image denoising. IEEE transactions on
image processing, 26(7), 3142-3155.
[24] Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep
45
network training by reducing internal covariate shift. In International conference
on machine learning (pp. 448-456). PMLR.
[25] Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2018). Residual dense network
for image super-resolution. In Proceedings of the IEEE conference on computer
vision and pattern recognition (pp. 2472-2481).
[26] Zhang, Y., Tian, Y., Kong, Y., Zhong, B., & Fu, Y. (2020). Residual dense network
for image restoration. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 43(7), 2480-2495.
[27] Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2003, November). Multiscale
structural similarity for image quality assessment. In The Thrity-Seventh Asilomar
Conference on Signals, Systems & Computers, 2003 (Vol. 2, pp. 1398-1402). Ieee.
[28] Wang, Z., & Bovik, A. C. (2009). Mean squared error: Love it or leave it? A new
look at signal fidelity measures. IEEE signal processing magazine, 26(1), 98-117.
[29] Van Der Jeught, S., Muyshondt, P. G., & Lobato, I. (2021). Optimized loss function
in deep learning profilometry for improved prediction performance. Journal of
Physics: Photonics, 3(2), 024014.
[30] Johnson, J., Alahi, A., & Fei-Fei, L. (2016, October). Perceptual losses for realtime style transfer and super-resolution. In European conference on computer
vision (pp. 694-711). Springer, Cham.
[31] Zuiderveld, K. (1994). Contrast limited adaptive histogram equalization. Graphics
gems, 474-485.
[32] Loog, M., van Ginneken, B., & Schilham, A. M. (2006). Filter learning:
application to suppression of bony structures from chest radiographs. Medical
image analysis, 10(6), 826-840.
[33] Chen, Y., Gou, X., Feng, X., Liu, Y., Qin, G., Feng, Q., ... & Chen, W. (2019).
Bone suppression of chest radiographs with cascaded convolutional networks in
wavelet domain. IEEE Access, 7, 8346-8357.
[34] Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for
semantic segmentation. In Proceedings of the IEEE conference on computer vision
and pattern recognition (pp. 3431-3440).
[35] Uggla, G., & Horemuz, M. (2021). Towards synthesized training data for semantic
segmentation of mobile laser scanning point clouds: Generating level crossings
from real and synthetic point cloud samples. Automation in Construction, 130,
103839.
[36] Deng, L. (2012). The mnist database of handwritten digit images for machine
learning research. IEEE Signal Processing Magazine, 29(6), 141–142.
[37] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image
recognition. In Proceedings of the IEEE conference on computer vision and
46
pattern recognition (pp. 770-778).
[38] Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely
connected convolutional networks. In Proceedings of the IEEE conference on
computer vision and pattern recognition (pp. 4700-4708).
[39] Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2015). Loss functions for neural
networks for image processing. arXiv preprint arXiv:1511.08861.
[40] Mahdianpari, M., Salehi, B., Rezaee, M., Mohammadimanesh, F., & Zhang, Y.
(2018). Very deep convolutional neural networks for complex land cover mapping
using multispectral remote sensing imagery. Remote Sensing, 10(7), 1119.