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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 吳文超 | zh_TW |
dc.contributor.advisor | Wen-Chau Wu | en |
dc.contributor.author | 陳冠君 | zh_TW |
dc.contributor.author | Kuan-Chun Chen | en |
dc.date.accessioned | 2023-03-27T17:02:15Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-03-30 | - |
dc.date.issued | 2022 | - |
dc.date.submitted | 2002-01-01 | - |
dc.identifier.citation | Prince, Martin James, et al. "World Alzheimer Report 2016-Improving healthcare for people living with dementia: Coverage, quality and costs now and in the future." (2016).
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86802 | - |
dc.description.abstract | 隨著醫療技術發展與衛生條件改善,全球人口逐漸高齡化,罹患失智症的人數不斷攀升。阿茲海默症(Alzheimer's Disease,AD)是目前最常見的失智症類型,尚無有效的治癒療程,僅能仰賴藥物控制。因此,AD的早期診斷及定期追蹤具有臨床重要性,其中又以低侵入性技術最符合風險利益比,例如磁振造影(Magnetic Resonance Imaging,MRI)。此外,多數失智症的影像深度學習研究為了提高分類準確度,經常會加入各式影像前處理、甚至預先切割出海馬迴或其他預設區域,限縮模型學習特定區域的體積或形狀變化等解剖訊息。如此做法雖然能剔除不具訊息的影像,提高模型收斂效率,但似乎違反了深度學習自動尋找特徵的本質。基於上述,本研究提出VGG-based 3D CNN模型:結合三維卷積神經網路(Convolutional Neural Networks,CNN)及VGG架構,改良架構配置,並將全連接層替換為Global Average Pooling。本研究以全大腦MRI影像作為訓練資料集,除了頭皮剝離及影像信號正規化,不做其他影像前處理,藉此驗證深度學習模型從MRI影像學習大腦組織結構特徵之能力,並以Grad-CAM視覺化結果檢視模型判別AD的依據是否符合臨床診斷。實驗結果顯示VGG-based 3D CNN診斷AD的準確度可達87.5%,模型分類主要關注側腦室、腦溝和腦皮質外圍,和臨床的專家經驗與研究相符—證明我們提出的VGG-based 3D CNN模型確實能學習到具有臨床意義的AD大腦影像特徵,具有一定的診斷準確度。值得一提的是,模型在不同資料集個案中的關注結構區域非常相似,這也證明VGG-based 3D CNN模型能驗證不同來源的影像資料集,對於臨床應用更具潛力。 | zh_TW |
dc.description.abstract | With the rapid aging of the global population, the number of people living with dementia is growing. Alzheimer's disease (AD) is the most common form of dementia. As of now, there is no cure for AD and medication only slows disease progression. Therefore, early diagnosis and regular monitoring of AD are of clinical importance, preferably by using techniques with minimal invasiveness and a reasonable risk-benefit ratio, such as magnetic resonance imaging (MRI). Additionally, most deep-learning-based imaging studies of dementia apply various image preprocessing and some even pre-segment anatomical regions such as hippocampus to improve the accuracy of classification or prediction. Constraining the model to learn structural features such as volume and shape from predetermined regions of interest may improve the converging speed. However, removing regions assumed to be uninformative seems to conflict with the fundamental strength of deep learning. In light of the above, we proposed a VGG-based three-dimensional convolutional neural network (3D CNN) model where the original VGG configuration was modified and the fully-connected layer was replaced with global average pooling. MRI images of the whole cerebrum were used as the training and testing datasets with minimal preprocessing (skull/scalp stripping and signal normalization) to validate the ability of the model to learn the spatial information from MRI images. Then, gradient-weighted class activation mapping (Grad-CAM) was used to visualize whether the model picked up the anatomical features comparable with those previously reported and applied in clinical diagnosis. Our results showed that VGG-based 3D CNN achieved an accuracy of 87.5% for AD diagnosis and mainly focused on the ventricle, sulcus, and surrounding areas of the cortex. It was also noted that our model picked up highly similar anatomical regions in different datasets. In conclusion, the VGG-based 3D CNN model proposed in this study was able to detect AD with a satisfactory accuracy rate based on clinically interpretable anatomical features. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-03-27T17:02:15Z
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dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES ix LIST OF TABLES xi Chapter 1 Introduction 1 1.1 Background 1 1.2 Aim of the present study 2 1.3 Literature survey 3 1.3.1 Traditional methods 3 1.3.2 Machine learning methods 3 1.3.3 Deep learning methods 4 1.3.4 Deep learning methods applied to AD classification 6 Chapter 2 Material and methods 8 2.1 Datasets 8 2.1.1 ADNI database 8 2.1.2 National Taiwan University Hospital dataset 10 2.2 Image preprocessing 12 2.3 Data augmentation 14 2.4 Convolutional Neural Network 17 2.4.1 Convolutional layer 17 2.4.2 Pooling layer 18 2.4.3 Fully connected layer 19 2.4.4 Activation function 21 2.5 VGG-based 3D CNN 26 2.6 Visualization method - Grad-CAM 28 2.7 Evaluation metrics 30 Chapter 3 Results and discussion 32 3.1 Dataset configuration 32 3.2 Experimental configuration 33 3.2.1 Experimental environment configuration 33 3.2.2 Experimental parameter configuration 33 3.3 Experimental results 33 3.4 Effectiveness of preprocessing 35 3.5 Comparison of model architecture improvement 38 3.6 Parameters analysis 41 3.6.1 Model convergence Analysis 41 3.6.2 Effectiveness of batch size 42 3.6.3 Effectiveness of learning rate 43 3.7 Visualization results of Grad-CAM 45 3.8 Comparison of published methods 49 Chapter 4 Conclusions 50 Appendix 51 Appendix I: Selected source code of Grad-CAM 52 Appendix II: Chinese version of the thesis 56 圖目錄 57 表目錄 59 第 1 章 緒論 60 1.1 研究背景 60 1.2 研究目的 61 1.3 文獻探討 62 1.3.1 傳統方法 62 1.3.2 機器學習 62 1.3.3 深度學習 63 1.3.4 深度學習應用於AD分類任務 64 第 2 章 研究材料與方法 67 2.1 研究資料集 67 2.1.1 ADNI 資料 67 2.1.2 臺大醫院資料 70 2.2 影像前處理 71 2.3 資料擴增 72 2.4 卷積神經網路 75 2.4.1 卷積層 75 2.4.2 池化層 76 2.4.3 全連接層 77 2.4.4 激活函數 80 2.5 VGG-based 3D CNN 84 2.6 視覺化模型Grad-CAM 86 2.7 評估指標 88 第 3 章 研究結果 90 3.1 資料集配置 90 3.2 研究配置 91 3.2.1 研究環境配置 91 3.2.2 研究參數配置 91 3.3 實驗結果 92 3.4 前處理效果比較 94 3.5 模型架構改進效果比較 97 3.6 參數分析 100 3.6.1 模型收斂分析 100 3.6.2 Batch size的影響 101 3.6.3 Learning rate 的影響 102 3.7 視覺化分析結果 104 3.8 不同方法之比較 108 第 4 章 研究討論 109 4.1 視覺化分析3D CNN之AD診斷 109 4.2 模型準確度探討 110 第 5 章 結論 111 REFERENCE 112 | - |
dc.language.iso | en | - |
dc.title | 以3D卷積神經網路偵測阿玆海默症之全大腦視覺化分析 | zh_TW |
dc.title | Whole-cerebrum visualization of 3D convolutional neural network-based detection of Alzheimer's disease | en |
dc.title.alternative | Whole-cerebrum visualization of 3D convolutional neural network-based detection of Alzheimer's disease | - |
dc.type | Thesis | - |
dc.date.schoolyear | 111-1 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 陳雅芳;黃騰毅 | zh_TW |
dc.contributor.oralexamcommittee | Ya-Fang Chen;Teng-Yi Huang | en |
dc.subject.keyword | 深度學習,三維卷積神經網路,阿茲海默症,磁振造影,電腦輔助診斷系統, | zh_TW |
dc.subject.keyword | Deep learning,3D convolutional neural network,Alzheimer's disease,Magnetic resonance imaging,Computer-aided diagnosis system, | en |
dc.relation.page | 123 | - |
dc.identifier.doi | 10.6342/NTU202203818 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2022-09-23 | - |
dc.contributor.author-college | 醫學院 | - |
dc.contributor.author-dept | 醫療器材與醫學影像研究所 | - |
顯示於系所單位: | 醫療器材與醫學影像研究所 |
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