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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許文翰(Wen-Hann Sheu) | |
| dc.contributor.author | Chi-Meng Wong | en |
| dc.contributor.author | 王志明 | zh_TW |
| dc.date.accessioned | 2021-06-17T09:07:57Z | - |
| dc.date.available | 2019-12-02 | |
| dc.date.copyright | 2019-12-02 | |
| dc.date.issued | 2019 | |
| dc.date.submitted | 2019-11-25 | |
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Support-Vector Networks, volume 20, pages 273–297. Kluwer Academic Publishers, 1995. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/74807 | - |
| dc.description.abstract | 本論文使用機器學習的方法來定位肺結節的位置，以及判斷該肺結節是否已發生病變。本文提出了一種U-net的變型方法，用於肺結節的分割和檢測。此外，本文通過使用肺結節分割的預測提供額外的信息，探索了肺結節檢測的二元分類任務。最後，對於分段任務，變型u-net的dice coefficient可達到0.8182；對於二進制分類任務，其錯誤率僅為4.22%。 | zh_TW |
| dc.description.abstract | In this thesis, lung nodule segmentation and lung nodule classification have been performed using database Image Database Consortium and Image Database Resource Initiative (LIDC). A variant U-net is proposed in this article for lung nodule segmentation and detection. Also, the binary classification task of lung nodule detection has been by using the predict of lung nodule segmentation as extra information. Finally, a variant u-net a dice coefficient of 0.8182 for segmentation task and an error rate of 4.22% for binary classification task have been accomplished. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T09:07:57Z (GMT). No. of bitstreams: 1 ntu-108-R06525065-1.pdf: 17776248 bytes, checksum: 579cfacaec91012a4e6618376fbdc5d4 (MD5) Previous issue date: 2019 | en |
| dc.description.tableofcontents | 摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Chapter 2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1 Computing Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 LIDC-IDRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 CT Scan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.3.1 Hounsfield Units . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.4 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.1 Morphology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.4.2 Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.4.3 Flood Fill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.5 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.1 Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.5.2 Convolutional Neural Network . . . . . . . . . . . . . . . . . . . 15 2.5.3 Recurrent Neural Network . . . . . . . . . . . . . . . . . . . . . 17 2.5.4 Activation Function . . . . . . . . . . . . . . . . . . . . . . . . . 18 Chapter 3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1 Image Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3.1.1 Image Processing . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.1.2 Data Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2 Tumor Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.1 Convolutional LSTM . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2.2 U-Net . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.2.3 Fully Connected Block . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.4 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2.5 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3 Tumor Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Dense Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 3.3.3 Loss Function . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Chapter 4 Evaluation & Results . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1 Experimental Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.1 Tumor Segmentation . . . . . . . . . . . . . . . . . . . . . . . . 37 4.1.2 Tumor Classification . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2 Computational Resource . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Chapter 5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Chapter A Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 A.1 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 A.1.1 PCA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 A.1.2 NMF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 A.1.3 Autoencoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 A.1.4 Triplet Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 A.1.5 Triplet Loss with Group Setting . . . . . . . . . . . . . . . . . . 66 A.2 Assessment of Classifications with/without Feature Extraction . . . . . . 67 A.2.1 DataSet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 A.2.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 A.2.3 Experimental Setting . . . . . . . . . . . . . . . . . . . . . . . . 70 A.2.4 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 A.2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 | |
| dc.language.iso | zh-TW | |
| dc.subject | U-net | zh_TW |
| dc.subject | 肺結節分類 | zh_TW |
| dc.subject | 影像辦識 | zh_TW |
| dc.subject | 肺結節的分割 | zh_TW |
| dc.subject | 高效能計算 | zh_TW |
| dc.subject | lung nodule segmentation | en |
| dc.subject | image processing | en |
| dc.subject | u-net | en |
| dc.subject | HPC | en |
| dc.subject | lung nodule classification | en |
| dc.title | 在多GPU架構下以深度學習方法篩檢肺腫瘤及確認其良惡性 | zh_TW |
| dc.title | Deep Learning on Lung Tumor Detection and Classification using multiple GPUs | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 張瑞益(Ray-I Chang),洪士灝(Shih-Hao Hung),張子明,陳基宏(Chi-Hung Chen) | |
| dc.subject.keyword | 影像辦識,U-net,肺結節的分割,肺結節分類,高效能計算, | zh_TW |
| dc.subject.keyword | image processing,u-net,lung nodule segmentation,lung nodule classification,HPC, | en |
| dc.relation.page | 81 | |
| dc.identifier.doi | 10.6342/NTU201904302 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2019-11-26 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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