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研究生:汪傳崴
研究生(外文):Chuan-Wei Wang
論文名稱:基於深度學習捲積神經網路之電腦斷層掃描肺腺癌肋膜侵犯預測模型
論文名稱(外文):Prediction Model of Visceral Pleural Invasion in Lung Adenocarcinoma on Computed Tomography Based on Deep Learning Convolutional Neural Network
指導教授:陳中明陳中明引用關係
指導教授(外文):Chung-Ming Chen
口試委員:林孟暐李佳燕
口試委員(外文):Mong-Wei LinChia-Yen Lee
口試日期:2022-02-08
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:醫學工程學研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2022
畢業學年度:110
語文別:中文
論文頁數:56
中文關鍵詞:肺腫瘤肋膜侵犯分類深度學習捲積神經網路注意力機制深度學習模型可視化
外文關鍵詞:Classification of visceral pleural invasion of lung tumorsdeep learningconvolutional neural networkattention mechanismvisualization of deep learning model
DOI:10.6342/NTU202200332
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  • 點閱點閱:7
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論文口試委員審定書……………………………………………………………………I
致謝……………………………………………………………………………………...II
摘要....………………………………………………………………………………….III
Abstract………………………………………………………………………………….V
目錄……………………………………………………………………………………VII
圖目錄………………………………………………………………………………….IX
表目錄………………………………………………………………………………….XI
第一章 緒論………………………………..……………………………..1
1.1 研究背景……………………………….….……………………..1
1.2 研究動機及目的………………………….…….………………..6
第二章 文獻回顧……………….…….…………………………………..8
2.1 影像形態學特徵……………….………………….……………..8
2.2 深度學習…………………………………….…….……………10
第三章 研究材料與方法………………………………………..………12
3.1 研究材料……………………………….…….…………………12
3.2 研究方法.......................12
3.2.1 Image preprocessing and augmentation…………..……………..13
3.2.2 4 Layers Convolutional Neural Network (4 Layers CNN)………14
3.2.3 Attention mechanism……………………………………………19
3.2.3.1 Squeeze and Excitation block……………………………………19
3.2.3.2 Dilate convolution block………………………………………...22
3.2.3.3 Lung map segmentation block…………………………………..24
3.3 Visualizing Deep Learning Model………………………………27
3.4 Performance Matrix……………………………………………..28
第四章 研究結果與討論………………………………………………..31
4.1 4 Layers Convolutional Neural Network之結果……………….31
4.2 添加Squeeze and Excitation block之結果…………………….34
4.3 添加Dilate convolution block之結果………………………….37
4.4 添加Lung map segmentation block之結果……………………40
4.5 與臨床醫師判別結果比較……………………………………..43
第五章 結論與未來展望………………………………………………..49
5.1 結論……………………………………………………………..49
5.2 未來展望………………………………………………………..50
Reference……………………………………………………………………………….52
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