請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70120
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 黃鐘揚(Chung-Yang (Ric) | |
dc.contributor.author | Kuan-Lun Tseng | en |
dc.contributor.author | 曾冠綸 | zh_TW |
dc.date.accessioned | 2021-06-17T03:44:35Z | - |
dc.date.available | 2020-02-23 | |
dc.date.copyright | 2018-02-23 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-02-01 | |
dc.identifier.citation | [1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Is- ard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Tal- war, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow.org.
[2] V. Badrinarayanan, A. Handa, and R. Cipolla. Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv preprint arXiv:1505.07293, 2015. [3]G.J.Brostow,J.Shotton,J.Fauqueur,andR.Cipolla.Segmentation and recognition using structure from motion point clouds. In ECCV, 2008. [4] H. Cai, R. Verma, Y. Ou, S.-k. Lee, E. R. Melhem, and C. Davatzikos. Probabilistic segmentation of brain tumors based on multi-modality magnetic resonance images. In IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007. [5] H. Chen, X. Qi, L. Yu, and P.-A. Heng. Dcan: Deep contour-aware networks for accurate gland segmentation. arXiv preprint arXiv:1604.02677, 2016. [6] J. Chen, L. Yang, Y. Zhang, M. Alber, and D. Z. Chen. Combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation. NIPS, 2016. [7] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062, 2014. [8] D. Eigen and R. Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. In ICCV, 2015. [9] B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik. Hypercolumns for object segmentation and fine-grained localization. In CVPR, 2015. [10] M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.- M. Jodoin, and H. Larochelle. Brain tumor segmentation with deep neural networks. Medical Image Analysis, 2016. [11] S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 1997. [12] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. [13] M. Kistler, S. Bonaretti, M. Pfahrer, R. Niklaus, and P. Büchler. The virtual skeleton database: An open access repository for biomedical research and collaboration. Journal of Medical Internet Reserach, 2013. [14] M. Lai. Deep learning for medical image segmentation. arXiv preprint arXiv:1505.02000, 2015. [15] J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. [16] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Bur- ren, N. Porz, J. Slotboom, R. Wiest, et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE Transactions on Medical Imaging, 2015. [17] B. H. Menze, K. Van Leemput, D. Lashkari, M.-A. Weber, N. Ayache, and P. Golland. A generative model for brain tumor segmentation in multi-modal images. In MICCAI, 2010. [18] H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. In ICCV, 2015. [19] S. Pereira, A. Pinto, V. Alves, and C. Silva. Brain tumor segmentation using convolutional neural networks in mri images. IEEE Transactions on Medical Imaging, 2016. [20] O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In MICCAI, 2015. [21] A. M. Saxe, J. L. McClelland, and S. Ganguli. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. arXiv preprint arXiv:1312.6120, 2013. [22] S. Xingjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-k. Wong, and W.-c. Woo. Convolu- tional lstm network: A machine learning approach for precipitation nowcasting. In NIPS, 2015. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70120 | - |
dc.description.abstract | 深度學習如卷積類神經網路在生醫影像上面已經被廣泛的運用而 且取得了突破性的成績。但是多數的方法並沒有採用多種圖像來源或 是僅僅把多種來源當成不同的頻道來使用。為了要把多型態的圖片的 資訊結合起來,本論文提出一個深度學習模型結合跨型態卷積利用在 核磁共振的腦部影像中。我們更利用序列學習中的卷積記憶網路把二 維序列所富涵的資訊結合。整個模型是可以直接做最佳化的,且為了 解決細胞分佈嚴重不平衡的問題我們利用平衡權重的方式來解決。在 BRATS-2015 的資料集中顯示我們的方法是比目前的方法都還要優秀的 | zh_TW |
dc.description.abstract | Deep learning models such as convolutional neural network have been widely used in 3D biomedical segmentation and achieve state-of-the-art performance. However, most of them often adapt a single modality or stack multiple modalities as different input channels. To better leverage the multi- modalities, we propose a deep encoder-decoder structure with cross-modality convolution layers to incorporate different modalities of MRI data. In addition, we exploit convolutional LSTM to model a sequence of 2D slices, and jointly learn the multi-modalities and convolutional LSTM in an end-to-end manner. To avoid converging to the certain labels, we adopt a re-weighting scheme and two-phase training to handle the label imbalance. Experimental results on BRATS-2015 [13] show that our method outperforms state-of-the- art biomedical segmentation approaches. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T03:44:35Z (GMT). No. of bitstreams: 1 ntu-107-R04921120-1.pdf: 1243752 bytes, checksum: efcd6ffdac7d4126772dc7f980285cec (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 1.Introduction 1
2.Related Work 4 3.Method 6 3.0.1 Encoder and Decoder 6 3.0.2 Multi-ResolutionFusion(MRF) 7 3.0.3 Cross-ModalityConvolution(CMC) 8 3.0.4 SliceSequenceLearning 8 4. Experiment 11 4.0.1 Dataset 11 4.0.2 Training 12 4.0.3 Baseline 15 4.0.4 ExperimentalResults 15 Conclusion 18 Bibliography 19 | |
dc.language.iso | en | |
dc.title | 結合序列與跨型態學習運用在 3D 生醫影像分割 | zh_TW |
dc.title | Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 徐宏民(Winston Hsu) | |
dc.contributor.oralexamcommittee | 黃寶儀(Polly Huang),陳文進(WC Chen),葉梅珍(Mei-Chen Yeh) | |
dc.subject.keyword | 深度學習,影像分割, | zh_TW |
dc.subject.keyword | deep learning,image segmentation, | en |
dc.relation.page | 21 | |
dc.identifier.doi | 10.6342/NTU201800241 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-02-02 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
顯示於系所單位: | 電機工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-107-1.pdf 目前未授權公開取用 | 1.21 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。