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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 張瑞峰 | |
dc.contributor.author | Chu-Hsuan Lee | en |
dc.contributor.author | 李竺軒 | zh_TW |
dc.date.accessioned | 2021-06-17T08:10:34Z | - |
dc.date.available | 2021-08-19 | |
dc.date.copyright | 2019-08-19 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-08-15 | |
dc.identifier.citation | [1] R. L. Siegel, K. D. Miller, and A. Jemal, 'Cancer statistics, 2019,' CA: a cancer journal for clinicians, vol. 69, no. 1, pp. 7-34, Jan 2019.
[2] R. J. Hooley, L. M. Scoutt, and L. E. Philpotts, 'Breast ultrasonography: state of the art,' Radiology, vol. 268, no. 3, pp. 642-659, Sep 2013. [3] D.-R. Chen, R.-F. Chang, W.-J. Kuo, M.-C. Chen, and Y.-L. Huang, 'Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks,' Ultrasound in medicine & biology, vol. 28, no. 10, pp. 1301-1310, Oct 2002. [4] M.-C. Yang et al., 'Robust texture analysis using multi-resolution gray-scale invariant features for breast sonographic tumor diagnosis,' IEEE Transactions on Medical Imaging, vol. 32, no. 12, pp. 2262-2273, Dec 2013. [5] J.-Z. Cheng et al., 'Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans,' Scientific reports, vol. 6, p. 24454, Apr 2016. [6] W. K. Moon, Y.-W. Shen, C.-S. Huang, L.-R. Chiang, and R.-F. Chang, 'Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images,' Ultrasound in medicine & biology, vol. 37, no. 4, pp. 539-548, Apr 2011. [7] A. Krizhevsky, I. Sutskever, and G. E. Hinton, 'Imagenet classification with deep convolutional neural networks,' in Advances in neural information processing systems, 2012, pp. 1097-1105. [8] K. He, X. Zhang, S. Ren, and J. Sun, 'Deep residual learning for image recognition,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. [9] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, 'You only look once: Unified, real-time object detection,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779-788. [10] J. Long, E. Shelhamer, and T. Darrell, 'Fully convolutional networks for semantic segmentation,' in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431-3440. [11] G. Litjens et al., 'A survey on deep learning in medical image analysis,' Medical image analysis, vol. 42, pp. 60-88, Dec 2017. [12] M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe, and S. Mougiakakou, 'Lung pattern classification for interstitial lung diseases using a deep convolutional neural network,' IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1207-1216, Feb 2016. [13] D. Lévy and A. Jain, 'Breast mass classification from mammograms using deep convolutional neural networks,' arXiv preprint arXiv:1612.00542, 2016. [14] O. Ronneberger, P. Fischer, and T. Brox, 'U-net: Convolutional networks for biomedical image segmentation,' in International Conference on Medical image computing and computer-assisted intervention, 2015, pp. 234-241. [15] K. He, G. Gkioxari, P. Dollár, and R. Girshick, 'Mask r-cnn,' in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961-2969. [16] B. S. Lin, K. Michael, S. Kalra, and H. R. Tizhoosh, 'Skin lesion segmentation: U-nets versus clustering,' in 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 2017, pp. 1-7. [17] M. U. Dalmış et al., 'Using deep learning to segment breast and fibroglandular tissue in MRI volumes,' Medical physics, vol. 44, no. 2, pp. 533-546, Dec 2017. [18] Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, '3D U-Net: learning dense volumetric segmentation from sparse annotation,' in International conference on medical image computing and computer-assisted intervention, 2016, pp. 424-432. [19] S. Sabour, N. Frosst, and G. E. Hinton, 'Dynamic routing between capsules,' in Advances in neural information processing systems, 2017, pp. 3856-3866. [20] G. E. Hinton, A. Krizhevsky, and S. D. Wang, 'Transforming auto-encoders,' in International Conference on Artificial Neural Networks, 2011, pp. 44-51. [21] Y. LeCun, Y. Bengio, and G. Hinton, 'Deep learning,' nature, vol. 521, no. 7553, p. 436, May 2015. [22] K. He, X. Zhang, S. Ren, and J. Sun, 'Identity mappings in deep residual networks,' in European conference on computer vision, 2016, pp. 630-645. [23] Y. Wu and K. He, 'Group normalization,' in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 3-19. [24] Y.-S. Huang, E. Takada, S. Konno, C.-S. Huang, M.-H. Kuo, and R.-F. Chang, 'Computer-Aided tumor diagnosis in 3-D breast elastography,' Computer methods and programs in biomedicine, vol. 153, pp. 201-209, Jan 2018. [25] Y. Bengio, P. Simard, and P. Frasconi, 'Learning long-term dependencies with gradient descent is difficult,' IEEE transactions on neural networks, vol. 5, no. 2, pp. 157-166, Mar 1994. [26] S. Ioffe and C. Szegedy, 'Batch normalization: Accelerating deep network training by reducing internal covariate shift,' arXiv preprint arXiv:1502.03167, 2015. [27] V. Nair and G. E. Hinton, 'Rectified linear units improve restricted boltzmann machines,' in Proceedings of the 27th international conference on machine learning (ICML-10), 2010, pp. 807-814. [28] X. Glorot, A. Bordes, and Y. Bengio, 'Deep sparse rectifier neural networks,' in Proceedings of the fourteenth international conference on artificial intelligence and statistics, 2011, pp. 315-323. [29] R. Kohavi, 'A study of cross-validation and bootstrap for accuracy estimation and model selection,' in Ijcai, 1995, vol. 14, no. 2, pp. 1137-1145. [30] J. A. Hanley and B. J. McNeil, 'The meaning and use of the area under a receiver operating characteristic (ROC) curve,' Radiology, vol. 143, no. 1, pp. 29-36, Apr 1982. [31] E. Xi, S. Bing, and Y. Jin, 'Capsule network performance on complex data,' arXiv preprint arXiv:1712.03480, 2017. [32] A. Jaiswal, W. AbdAlmageed, Y. Wu, and P. Natarajan, 'Capsulegan: Generative adversarial capsule network,' in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 0-0. [33] A. Romero, N. Ballas, S. E. Kahou, A. Chassang, C. Gatta, and Y. Bengio, 'Fitnets: Hints for thin deep nets,' arXiv preprint arXiv:1412.6550, 2014. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/73801 | - |
dc.description.abstract | 乳癌是女性最常見的癌症之一,因此早期的偵測、診斷和治療是減低病人的死亡的最好方法。全自動乳房超音波(Automated Breast Ultrasound, ABUS)因可提供醫生完整的三維乳房影像資訊所以廣泛用於腫瘤偵測,然而大量的影像使得醫生需要花費較多時間檢閱影像、確定腫瘤位置、以及預先腫瘤良惡性。近來,基於卷積神經網路(Convolutional Neural Network, CNN)開發的電腦輔助診斷系統證實CNN能從影像中自動學習紋理與形狀特徵並提升醫生的診斷率,然而,卷積神經網路有著對於物體旋轉和對於特徵之間的相對關係學習不佳的問題。2017年,一個淺層且以向量方式呈現特徵的膠囊網路被提出解決卷積神經網路的問題,但也因為層數少的關係會有不易從影像中學習較複雜特徵的問題。因此,本研究提出一個包含3-D U型網路與改進的3-D膠囊網路的電腦輔助診斷系統,首先,從ABUS影像中取得腫瘤範圍,接著透過3-D U型網路取得腫瘤遮罩,最後,再利用改進的3-D膠囊網路同時取得紋理與形態特徵進行腫瘤診斷。論文中,我們引入了3-D殘差塊提高診斷腫瘤良惡性的準確率,此外,由於全自動乳房超音波影像資訊與膠囊網路使得系統訓練時限制了批量集的大小影響系統準確率,我們更採用組標準化取代原本的批量標準化解決此問題提升系統準確率。實驗中共使用了446筆全自動乳房超音波產生的乳房腫瘤影像,其中包含229個惡性腫瘤和217個良性腫瘤。根據實驗顯示,我們提出的方法達到準確率85.20%、靈敏性87.34%、特異性82.95%和ROC曲線下面積0.9134的成果,顯示系統是有能力在ABUS影像上進行腫瘤診斷。 | zh_TW |
dc.description.abstract | Breast cancer is one of the most common cancer in the female. Early detection, diagnosis, and treatment is the best way for reducing mortality. The automated breast ultrasound (ABUS) had been widely used for breast tumor detection since it can provide the three-dimensional (3-D) volume of the breast for a physician to review. However, it is time-consuming for a physician to review the whole ABUS image, find out the suspicious lesion, and determine lesion as benign or malignant. Some computer-aided diagnosis (CADx) systems based on the convolution neural network (CNN) had been proposed and proven that CNN is a useful architecture to learn texture and shape features automatically for helping the physician make a diagnosis. However, CNN is poor at dealing with the spatial relationship between different features and object rotation. In 2017, a shallow network, capsule net (CapsNet), representing features as a vector was proposed for overcoming the problem. However, the shallow architecture was also the drawback that made the CapsNet hard to learn complex features from the image. Therefore, in this study, a CADx consisted of the 3-D U-net and the modified 3-D CapsNet network is proposed for tumor diagnosis in ABUS. First, the volume of interest (VOI) is cropped out from the ABUS image. Then, the VOI is input into the 3-D U-net model to generate the tumor mask. Afterward, the VOI and mask are delivered to the modified CapsNet for determining tumor as malignant or benign. In this study, to overcome the drawback of original CapsNet, the 3-D residual block is introduced into the CapsNet for learning high-level features from tumor image. Furthermore, the group normalization is also substituted for the batch normalization to address the limitation of batch size during training because the usage of 3-D input and capsule is memory demanding. In our experiments, there were 446 breast tumors images generated from automated breast ultrasound system (ABUS), which included 229 malignant tumors and 217 benign tumors. In our experiment result, the accuracy, sensitivity, specificity, and area under the curve (AUC) were 85.20%, 87.34%, 82.95% and 0.9134 respectively which outperformed other CNN models. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T08:10:34Z (GMT). No. of bitstreams: 1 ntu-108-R05922158-1.pdf: 1686275 bytes, checksum: c668c4ec3d2c1fa662f7bd08d4524871 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 i
致謝 ii 摘要 iii Abstract v Table of Contents vii List of Figures viii List of Tables ix Chapter 1 Introduction 1 Chapter 2 Material 5 Chapter 3 Methods 7 3.1 VOI Extraction 8 3.2 Tumor Segmentation 8 3.2.1 3-D U-net 9 3.2.2 Post-processing 10 3.3 Tumor Classification 11 3.3.1 The Modified 3-D CapsNet 12 Chapter 4 Experiment Result and Discussion 15 4.1 Result 15 4.2 Discussion 24 Chapter 5 Conclusion 28 Reference 30 | |
dc.language.iso | en | |
dc.title | 3D膠囊神經網路之乳房自動超音波電腦輔助腫瘤診斷 | zh_TW |
dc.title | 3D Capsule Neural Network on Automated Breast Ultrasound Tumor Diagnosis | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅崇銘,陳鴻豪 | |
dc.subject.keyword | 乳癌,全自動乳房超音波,三維殘差卷積神經網路,膠囊網路,組標準化,電腦輔助診斷, | zh_TW |
dc.subject.keyword | Breast cancer,automated breast ultrasound,3-D residual convolution neural network,capsule network,group normalization,computer-aided diagnosis, | en |
dc.relation.page | 32 | |
dc.identifier.doi | 10.6342/NTU201902942 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-08-16 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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