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系統識別號 U0026-0208201916352600
論文名稱(中文) 發展肝臟纖維化分類方法基於遷移學習與卷積神經網路於超音波影像
論文名稱(英文) Development of liver fibrosis classification methods based on transfer learning and convolutional neural network for ultrasound images
校院名稱 成功大學
系所名稱(中) 醫學資訊研究所
系所名稱(英) Institute of Medical Informatics
學年度 107
學期 2
出版年 108
研究生(中文) 李東璋
研究生(英文) Dong-Zhang Li
學號 Q56061058
學位類別 碩士
語文別 英文
論文頁數 51頁
口試委員 指導教授-王士豪
口試委員-林奕勳
口試委員-吳佳慶
口試委員-陳天送
口試委員-方佑華
中文關鍵字 超音波影像  纖維化肝臟  深度學習  卷積神經網路  可視化 
英文關鍵字 liver fibrosis  deep learning  convolutional neural network  visualization  ultrasound image 
學科別分類
中文摘要 根據世界衛生組織統計,全球約有三億人口罹患肝臟相關疾病,每年約一億人死於肝病,在台灣根據衛生福利部統計,每年約一萬多人死於肝病,是台灣第九大死因,癌症死亡人口第二名。肝病包含肝纖維化、肝炎、肝硬化和肝癌,原本柔軟的肝臟若長期發炎導致肝細胞遭受破壞,會刺激肝臟內的纖維母細胞,製造出膠原蛋白填補肝細胞壞死後留下的空間,若持續累積,就會朝向不可逆的肝硬化演進。近年來,早期發現和治療的醫學觀念逐漸普及,與其負擔發病後龐大的醫療成本,不如在早期就發現給予治療,對社會體系和個人的負擔都比較小,而腹部超音波影像就是一個有效檢查肝臟的工具,本研究利用非侵入式超音波醫療系統擷取 Sprague Drawley大鼠的肝臟影像進行分析。而深度學習領域中,卷積神經網路是目前最有效,提取資料訊息的工具,包含 LeNet,AlexNet,VGGNet, GoogLeNet, ResNet, SENet,然而這些神經網路各有優缺點,強大的辨識能力需要大量的運算資源和運算時間,因此,選擇適合資料型態的神經網路是必要的能力,了解分類纖維化程度後,再將網路所學習到的特徵,也就是影像上關鍵的特徵視覺化,打破深度學習中裡的黑盒子,使研究能夠更進一步。
英文摘要 According to the World Health Organization, there are around 300 million people have liver-related diseases. There are around 1 million people died because of liver-related diseases. According to the Taiwan Health Promotion, Ministry of Health and Welfare, liver-related diseases are the ninth top cause of death and second top cause death of cancer. Liver diseases include liver fibrosis, hepatitis, cirrhosis, and liver cancer. Hepatocyte would. In recent years, the idea of timely diagnoses become polular. Timely diagnoses and therapy may be practical and are less burden for societies and people. Abdominal ultrasound image is an effective test tool for the liver. This study uses a non-invasive ultrasound system to acuquire liver images from Sprague Drawley rats. In deep learning field, convolution neural network is the most powerful tool to extract information from data by methods which including the LeNet, VGGNet, GoogLeNet, ResNet, and SENet, etc. However, their strong discriminative abilities need large computation power and time. Choosing a suitable network and finding the trade-off between accuracy and computation time are important. After optimizing the accuracy of classification of liver fibrosis, visualizing key features what the network learn is a key point to improve the study which breaks the black box in the deep learning field.
論文目次 摘要 I
ABSTRACT II
誌謝 III
CONTENT IV
LIST OF FIGURES VI
LIST OF TABLES VII
CHAPTER 1 INTRODUCTION 1
1.1 Liver fibrosis 1
1.2 Ultrasound 3
1.3 Previous study 4
1.4 Deep learning 5
1.5 Motivation and objectives 6
CHAPTER 2 BACKGROUND 7
2.1 Fundamental of ultrasound 7
2.1.1 Ultrasound characteristics and propagation 7
2.1.2 Reflection and refraction 8
2.1.3 Attenuation and absorption 10
2.2 Convolutional neural network 11
2.3 Transfer learning 15
CHAPTER 3 MATERIALS AND METHODS 16
3.1 In vivo experiment 16
3.1.2 Animal model 17
3.2 Ultrasound system arrangement 17
3.3 Computing environment 21
3.4 Convolutional neural network models 22
3.4.1 Feature selection, model A 22
3.4.2 VGGNet with frozen blocks, model B 25
3.4.3 VGGNet with fine-tuning blocks, model C 26
3.4.4 Modified LeNet, model D 27
3.5 Visualization method – Class activation map 29
CHAPTER 4 RESULTS 30
4.1 Tissue cross section 30
4.2 Ultrasound liver image 32
4.3 Autoencoder 34
4.4 Accuracy 36
4.5 Visualization 37
CHAPTER 5 DISSCUSSION 40
5.1 Experiment arrangement 40
5.2 Computing environment building 41
5.3 Models’ performance 41
5.4 Autoencoder 42
5.5 Visualization 42
CHAPTER 6 CONCLUSION 44
6.1 Conclusion 44
6.2 Future work 44
6.2.1 Three-dimension analysis 45
6.2.2 Cross-modal and cross-domain studying 45
REFERENCES 46
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