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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張瑞峰(Ruey-Feng Chang) | |
dc.contributor.author | Sheng-Kai Huang | en |
dc.contributor.author | 黃聖凱 | zh_TW |
dc.date.accessioned | 2021-06-15T11:19:48Z | - |
dc.date.available | 2023-09-18 | |
dc.date.copyright | 2020-09-17 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-18 | |
dc.identifier.citation | [1] A. K. G. M. Bekas, K. Pachocki, “How often are X-rays used as diagnostic tool by healthcare providers in the Mazovian Province of Poland,” Rocz Panstw Zakl Hig, pp. 155-160, 2013. [2] G. C. o. D. Collaborators, “Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980–2017: a systematic analysis for the Global Burden of Disease Study 2017,” The Lancet, vol. 392, pp. 1736-1788, 2018. [3] N. G. Csikesz, and E. J. Gartman, “New developments in the assessment of COPD: early diagnosis is key,” International journal of chronic obstructive pulmonary disease, vol. 9, pp. 277-286, 2014. [4] S. F. Nemec, A. A. Bankier, and R. L. Eisenberg, “Pulmonary Hyperlucency in Adults,” American Journal of Roentgenology, vol. 200, no. 2, pp. W101-W115, 2013/02/01, 2013. [5] M.-A. Blanchette, and J.-M. Grenier, “Subtle radiographic presentation of a pleural effusion secondary to a cancer of unknown primary: a case study,” The Journal of the Canadian Chiropractic Association, vol. 58, no. 3, pp. 273-279, 2014. [6] E. J. Hwang, J. G. Nam, W. H. Lim, S. J. Park, Y. S. Jeong, J. H. Kang, E. K. Hong, T. M. Kim, J. M. Goo, S. Park, K. H. Kim, and C. M. Park, “Deep Learning for Chest Radiograph Diagnosis in the Emergency Department,” Radiology, vol. 0, pp. 191225, 2019. [7] Y. Sim, M. J. Chung, E. Kotter, S. Yune, M. Kim, S. Do, K. Han, H. Kim, S. Yang, D.-J. Lee, and B. W. Choi, “Deep Convolutional Neural Network–based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs,” Radiology, vol. 294, no. 1, pp. 199-209, 2020/01/01, 2019. [8] C. S. White, T. Flukinger, J. Jeudy, and J. J. Chen, “Use of a Computer-aided Detection System to Detect Missed Lung Cancer at Chest Radiography,” Radiology, vol. 252, no. 1, pp. 273-281, 2009/07/01, 2009. [9] Y. Jiang, R. M. Nishikawa, R. A. Schmidt, A. Y. Toledano, and K. Doi, “Potential of Computer-aided Diagnosis to Reduce Variability in Radiologists’ Interpretations of Mammograms Depicting Microcalcifications,” Radiology, vol. 220, no. 3, pp. 787-794, 2001/09/01, 2001. [10] B. V. Ginneken, B. M. T. H. Romeny, and M. A. Viergever, “Computer-aided diagnosis in chest radiography: a survey,” IEEE Transactions on Medical Imaging, vol. 20, no. 12, pp. 1228-1241, 2001. [11] A. Majkowska, S. Mittal, D. F. Steiner, J. J. Reicher, S. M. McKinney, G. E. Duggan, K. Eswaran, P.-H. Cameron Chen, Y. Liu, S. R. Kalidindi, A. Ding, G. S. Corrado, D. Tse, and S. Shetty, “Chest Radiograph Interpretation with Deep Learning Models: Assessment with Radiologist-adjudicated Reference Standards and Population-adjusted Evaluation,” Radiology, vol. 0, pp. 191293, 2019. [12] S. Pereira, A. Pinto, J. Amorim, A. Ribeiro, V. Alves, and C. A. Silva, “Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks,” IEEE Transactions on Medical Imaging, vol. 38, no. 12, pp. 2914-2925, 2019. [13] X. Li, H. Chen, X. Qi, Q. Dou, C. Fu, and P. Heng, “H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes,” IEEE Transactions on Medical Imaging, vol. 37, pp. 2663-2674, 2018. [14] E. Pesce, S. Joseph Withey, P.-P. Ypsilantis, R. Bakewell, V. Goh, and G. Montana, “Learning to detect chest radiographs containing pulmonary lesions using visual attention networks,” Medical Image Analysis, vol. 53, pp. 26-38, 2019/04/01/, 2019. [15] H. Salehinejad, E. Colak, T. Dowdell, J. Barfett, and S. Valaee, “Synthesizing Chest X-Ray Pathology for Training Deep Convolutional Neural Networks,” IEEE Transactions on Medical Imaging, vol. 38, pp. 1197-1206, 2019. [16] J. Shang, C. Xiao, T. Ma, H. Li, and J. Sun, “GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination,” in AAAI Conference on Artificial Intelligence (AAAI), 2019. [17] Z.-M. Chen, X.-S. Wei, P. Wang, and Y. Guo, “Multi-Label Image Recognition With Graph Convolutional Networks,” in 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [18] B. Chen, J. Li, G. Lu, and D. Zhang, “Lesion Location Attention Guided Network for Multi-Label Thoracic Disease Classification in Chest X-Rays,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 7, pp. 2016-2027, 2020. [19] B. Chen, J. Li, X. Guo, and G. Lu, “DualCheXNet: dual asymmetric feature learning for thoracic disease classification in chest X-rays,” Biomedical Signal Processing and Control, vol. 53, pp. 101554, 2019. [20] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778. [21] G. Huang, Z. Liu, L. van der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. [22] T. Kurmann, P. Márquez-Neila, S. Wolf, and R. Sznitman, “Deep Multi-label Classification in Affine Subspaces,” in 2019 Medical Image Computing and Computer Assisted Intervention (MICCAI), Cham, 2019, pp. 165-173. [23] H. Wang, H. Jia, L. Lu, and Y. Xia, “Thorax-Net: An Attention Regularized Deep Neural Network for Classification of Thoracic Diseases on Chest Radiography,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 2, pp. 475-485, 2020. [24] Y. Shen, and M. Gao, “Dynamic Routing on Deep Neural Network for Thoracic Disease Classification and Sensitive Area Localization,” in Machine Learning in Medical Imaging, Cham, 2018, pp. 389-397. [25] S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic Routing Between Capsules,” in 2017 Advances in Neural Information Processing Systems (NIPS), 2017, pp. 3856-3866. [26] X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 3462-3471. [27] B. Chen, J. Li, G. Lu, H. Yu, and D. Zhang, “Label Co-occurrence Learning with Graph Convolutional Networks for Multi-label Chest X-ray Image Classification,” IEEE Journal of Biomedical and Health Informatics, pp. 1, 2020. [28] A. I. Aviles-Rivero, N. Papadakis, R. Li, P. Sellars, Q. Fan, R. T. Tan, and C.-B. Schönlieb, “GraphXNET-Chest X-Ray Classification Under Extreme Minimal Supervision,” in 2019 Medical Image Computing and Computer Assisted Intervention (MICCAI), Cham, 2019, pp. 504-512. [29] J. Liu, G. Zhao, Y. Fei, M. Zhang, Y. Wang, and Y. Yu, “Align, Attend and Locate: Chest X-Ray Diagnosis via Contrast Induced Attention Network With Limited Supervision,” in 2019 IEEE International Conference on Computer Vision (ICCV), 2019. [30] Z. Li, C. Wang, M. Han, Y. Xue, W. Wei, L. Li, and L. Fei-Fei, “Thoracic Disease Identification and Localization with Limited Supervision,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 8290-8299. [31] D. Bahdanau, K. Cho, and Y. Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate,” in 2015 International Conference on Learning Representations (ICLR), 2015. [32] B. Knyazev, G. W. Taylor, and M. R. Amer, “Understanding attention in graph neural networks,” in 2019 Conference on Neural Information Processing Systems (NeurIPS), 2019. [33] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” in Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, Lake Tahoe, Nevada, 2012, pp. 1097–1105. [34] M. Tan, and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in 2019 International Conference on Machine Learning (ICML), 2019. [35] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929-1958, 2014. [36] P. Baldi, and P. J. Sadowski, “Understanding Dropout,” in 2013 Advances in Neural Information Processing Systems (NIPS), 2013, pp. 2814-2822. [37] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” CoRR, vol. abs/1704.04861, /, 2017. [38] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [39] J. Hu, L. Shen, and G. Sun, “Squeeze-and-Excitation Networks,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132-7141. [40] Q. C. Min Lin, Shuicheng Yan, “Network In Network,” in 2014 International Conference on Learning Representations (ICLR), 2014. [41] M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, and Q. V. Le, “MnasNet: Platform-Aware Neural Architecture Search for Mobile,” in 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. [42] M. Nestor L. Müller, PhD, Richard S. Fraser, MD, Neil C. Colman, MD, and P. D. Pare, MD, Radiologic diagnosis of diseases of the chest: W.B. Saunders Company, 2001. [43] P. E. Marik, 'Pleural Effusions and Atelectasis,' Handbook of Evidence-Based Critical Care, pp. 271-278, New York, NY: Springer New York, 2010. [44] T. N. Kipf, and M. Welling, “Semi-Supervised Classification with Graph Convolutional Networks,” in 2017 International Conference on Learning Representations (ICLR), 2017. [45] P. Veličković, A. Casanova, P. Liò, G. Cucurull, A. Romero, and Y. Bengio, “Graph attention networks,” in 2018 International Conference on Learning Representations (ICLR), 2018. [46] L. McCauley, and N. Dean, “Pneumonia and empyema: causal, casual or unknown,” Journal of thoracic disease, vol. 7, no. 6, pp. 992-998, 2015. [47] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is All you Need,” in 2017 Advances in Neural Information Processing Systems (NIPS), 2017, pp. 5998-6008. [48] L. Li, Z. Gan, Y. Cheng, and J. Liu, “Relation-Aware Graph Attention Network for Visual Question Answering,” in 2019 The IEEE International Conference on Computer Vision (ICCV), 2019. [49] S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2017. [50] Y. Zheng, B. Jiang, J. Shi, H. Zhang, and F. Xie, “Encoding Histopathological WSIs Using GNN for Scalable Diagnostically Relevant Regions Retrieval,” in 2019 Medical Image Computing and Computer Assisted Intervention (MICCAI), Cham, 2019, pp. 550-558. [51] J. Pennington, R. Socher, and C. D. Manning, “GloVe: Global Vectors for Word Representation,” in 2014 Empirical Methods in Natural Language Processing (EMNLP), 2014, pp. 1532-1543. [52] T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal Loss for Dense Object Detection,” in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2999-3007. [53] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. [54] M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, “Transfusion: Understanding Transfer Learning with Applications to Medical Imaging,” in 2019 Conference on Neural Information Processing Systems (NeurIPS), 2019. [55] G. Xavier, and B. Yoshua, “Understanding the difficulty of training deep feedforward neural networks,” in 2010 Conference on Neural Information Processing Systems (NeurIPS), 2010, pp. 249-256. [56] L. Luo, Y. Xiong, Y. Liu, and X. Sun, “Adaptive Gradient Methods with Dynamic Bound of Learning Rate,” in 2019 International Conference on Learning Representations (ICLR), New Orleans, Louisiana, 2019. [57] S. Ioffe, and C. Szegedy, “Batch normalization: accelerating deep network training by reducing internal covariate shift,” in 2015 International Conference on International Conference on Machine Learning (ICML), Lille, France, 2015, pp. 448–456. [58] E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, “Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach,” Biometrics, vol. 44, no. 3, pp. 837-845, 1988. [59] W. J. YOUDEN, “Index for rating diagnostic tests.,” Cancer, vol. 3, pp. 32-35, 1950. [60] N. J. Perkins, and E. F. Schisterman, “The inconsistency of 'optimal' cutpoints obtained using two criteria based on the receiver operating characteristic curve.,” American journal of epidemiology, vol. 163, pp. 670-675, 2006. [61] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” in 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618-626. [62] A. R. O'Connor, and W. E. Morgan, “Radiological review of pneumothorax,” BMJ (Clinical research ed.), vol. 330, no. 7506, pp. 1493-1497, 2005. [63] H. Wahlgren, W. Mortensson, M. Eriksson, Y. Finkel, M. Forsgren, and M. Leinonen, “Radiological findings in children with acute pneumonia: age more important than infectious agent,” Acta Radiologica, vol. 46, no. 4, pp. 431-436, 2005/07/01, 2005. [64] A. N. Khan, H. Al-Jahdali, S. Al-Ghanem, and A. Gouda, “Reading chest radiographs in the critically ill (Part I): Normal chest radiographic appearance, instrumentation and complications from instrumentation,” Annals of thoracic medicine, vol. 4, no. 2, pp. 75-87, 2009. [65] S. A. Dosh, “Diagnosis of heart failure in adults.,” American family physician, vol. 70, pp. 2145-2152, 2004. [66] A. Saguil, K. Wyrick, and J. Hallgren, “Diagnostic approach to pleural effusion.,” American family physician, vol. 90, pp. 99-104, 2014. [67] E. Wey, and C. Kibbler, 'CHAPTER 40 - Infections associated with neutropenia and transplantation,' Antibiotic and Chemotherapy (Ninth Edition), R. G. Finch, D. Greenwood, S. R. Norrby and R. J. Whitley, eds., pp. 502-523, London: W.B. Saunders, 2010. [68] J. H. Ryu, E. J. Olson, D. E. Midthun, and S. J. Swensen, “Diagnostic approach to the patient with diffuse lung disease.,” Mayo Clinic proceedings, vol. 77, pp. 1221-7; quiz 1227, 2002. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/49221 | - |
dc.description.abstract | 胸部X光在臨床上是常用於診斷胸腔疾病的診斷工具之一,而胸腔疾病是造成世界各地生命損失的常見原因。為了降低胸腔疾病造成的死亡率,及早的發現與治療是非常重要的。電腦輔助診斷(Computer-aided Diagnosis, CAD)系統可以幫助放射科醫生識別胸腔疾病,進而提高放射科醫生的診斷準確度,並有助於及早開始治療過程。因此,我們提出一個電腦輔助診斷系統使用胸腔X光片來診斷胸腔疾病,透過卷積神經網絡(Convolutional Neural Network, CNN)擷取的影像特徵以及利用含圖注意力機制(Graph Attention Mechanism)的圖神經網路(Graph Neural Network, GNN)強化不同疾病之間的關係來改善診斷表現。在實驗中,我們利用來自於公開資料集(NIH Chest X-ray dataset)中共112,120張正面胸腔X光片,在此資料集中包含14種常見胸腔疾病標籤,而在這14種常見的胸腔疾病中每一張X光片可具有多個不同標籤,來評估提出的方法的表現。根據實驗結果,我們提出的方法表現優於先前研究相關的方法,在14種常見的胸腔疾病的平均曲線下區域(AUC)分數達到0.8266,而平均的準確度、靈敏度和特異性分別為0.7504,0.7704和0.7495。我們提出的方法整合了卷積神經網絡模型和包含圖注意力機制的圖神經網路模型並利用到疾病之間加強後的關聯性來改善診斷表現,而提出的方法可以有效的診斷胸腔疾病並有較好的診斷表現。 | zh_TW |
dc.description.abstract | The chest x-ray is one of the most common techniques in clinical for thorax disease diagnosis, and thorax diseases are common causes of global life loss. For thorax patients, early diagnosis and treatment could reduce the mortality rate. A good computer-aided diagnosis (CAD) system could not only help radiologists to recognize different thorax diseases but also improve the diagnosis performance. Hence, we proposed a thorax CAD system using chest x-ray images through the convolution neural network (CNN). The proposed CAD system extracted the image representation features of chest x-ray images and further employed the graph neural network (GNN) with graph attention mechanism to enhance the correlation between different diseases to improve thorax disease diagnosis performance. In the experiments, 112,120 frontal-view chest x-ray images with labels of 14 common thorax diseases from an open dataset (NIH Chest X-ray dataset) were used to evaluate the performance of the proposed method, each chest x-ray image could have multiple thorax diseases label. Based on the experimental results, the best diagnosis performance of our method is better than previous related works that the average AUC score of 14 common thorax diseases is 0.8266, and the average accuracy, sensitivity, and specificity are 0.7504, 0.7704, and 0.7495, respectively. In summary, the proposed method integrates the CNN model and the GNN model with the graph attention mechanism to diagnose thorax diseases efficiently and has better diagnosis performance. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T11:19:48Z (GMT). No. of bitstreams: 1 U0001-1208202017550100.pdf: 2620473 bytes, checksum: 64743de77bb2f361b28351e1ca3c70f9 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 口試委員會審定書 i 致謝 ii 摘要 iii Abstract iv Table of Contents vi List of Figures vii List of Tables viii Chapter 1 Introduction 1 Chapter 2 Materials 6 Chapter 3 Methods 9 3.1. CNN Backbone 9 3.1.1. EfficientNet 10 3.2. Graph Neural Networks (GNN) 14 3.2.1. GAT for Thorax Diseases Diagnosis 16 3.2.2. Correlation Weight Matrix of Thorax Diseases 18 3.3. CNN Model with Graph Attention Mechanism (CGAM architecture) 19 3.4. Experimental Setting 22 3.5. Evaluation and Statistics 23 Chapter 4 Experimental Results 25 4.1. Comparison of Different CNN Architectures 25 4.2. Comparison of CGAM Architectures 31 4.2.1. Analysis of Hyper-parameter for Correlation Weight Matrix 31 4.2.2. CGAM Architectures Based on Different CNN Backbone 35 4.2.3. The Effect of Graph Attention Mechanism 39 4.3. Comparison with CGAM and CGAM-L 41 4.4. Comparison with Previous Related Works 45 4.5. Visualization Results 47 4.6. Discussions 50 Chapter 5 Conclusions and Future Works 57 References 59 | |
dc.language.iso | en | |
dc.title | 電腦輔助診斷X光片胸腔疾病透過具圖注意力機制的卷積神經網路 | zh_TW |
dc.title | Computer-Aided Diagnosis of X-ray Thorax Diseases Using CNN Model with Graph Attention Mechanism | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 羅崇銘(Chung-Ming Lo),陳鴻豪(Hong-Hao Chen) | |
dc.subject.keyword | 胸部X光,胸腔疾病,電腦輔助診斷,圖神經網路,圖注意力機制,卷積神經網路, | zh_TW |
dc.subject.keyword | Chest x-ray,Thorax diseases,Computer-aided diagnosis,Graph neural network,Graph attention mechanism,Convolution neural network, | en |
dc.relation.page | 64 | |
dc.identifier.doi | 10.6342/NTU202003136 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-08-19 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
顯示於系所單位: | 資訊網路與多媒體研究所 |
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