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研究生(外文):Pu-Hsien Fong
論文名稱(外文):Computer-aided Cecum Detection Methodology for Cecal Intubation Rate Using Deep learning
指導教授(外文):Chung-Ping Chen
口試委員(外文):Chien-Mo LiHan-Mo ChiuWen-Shiang Chen
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在大腸鏡檢查中,有多項關於手術品質的指標,如盲腸到達率(Cecal Intubation Rate) 、腸道準備(Bowel Preparation) 、腺瘤偵測率(Adenoma Detection Rate) 、退出時間(Withdrawal Time) 等,研究發現,盲腸到達率高的醫生其病人得到大腸癌的機率相對較低,然而,現今僅能透過人工的方式,逐一地檢查手術報告中的每張照片是否為盲腸影像,人力成本高昂且耗時。因此,在本論文中,我們針對盲腸到達率提出一套新的盲腸偵測方法,透過深度學習和卷積神經網路,自動化判讀盲腸影像,藉此減輕醫師們的工作量,同時增加大腸鏡手術的品質。實驗結果顯示,我們提出的模型較先前的方法減少了84%的參數量,且提升了6%以上的準確率、24%的靈敏度、4.7%的特異度。除此之外,我們使用了三份大腸的開放資料來測試我們所訓練出來的分類器,最後達到超過99%的準確率,證明了我們的分類器沒有過適(overfitting)的問題。
In colonoscopy examination, there are several quality assessment indicators, such as Cecal Intubation Rate, Bowel Preparation, Adenoma Detection Rate, Withdrawal Time, etc. It has been proved that examiners with higher Cecal Intubation Rate often have lower chance of missing polyps which might turn into colorectal cancer. However, it is a time-consuming and labor-intensive work for reviewers to check if the colonoscopy examination reaches the cecum. Therefore, in this paper, we propose a novel methodology which adopts deep learning and convolutional neural network(CNN) techniques to detect cecum images. The proposed methodology automatically distinguishes cecum images from other colonoscopy images, thereby reducing the workload of doctors and increasing the quality of colonoscopy. Comparing with our previous work, the proposed methodology reduces 84% of parameter numbers of CNN architecture while improving the detecting accuracy by over 6%, sensitivity by 24%, and specificity by 4.7%. In addition, we test our model with 3 different open source datasets and reach over 99% accuracy, which means that the overfitting problem has been improved.
致謝 i
摘要 ii
Abstract iii
Table of Contents iv
List of Figures vi
List of Tables viii
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 3
1.3 Contribution 4
1.4 Organization 5
Chapter 2 Prior Works 6
2.1 Deep Convolutional Neural Networks 6
2.2 Colonoscopy Object Detection 14
Chapter 3 Datasets and Evaluation 16
3.1 Datasets Descriptions 16
3.2 Evaluation Metrics 20
Chapter 4 Proposed Methods 26
4.1 Image Pre-processing 27
4.2 Image Augmentation 28
4.3 Training Strategies 31
4.3.1 Loss Function 31
4.3.2 Optimizer 32
4.3.3 Learning Rate Scheduling 33
4.3.4 Model Architecture 34
Chapter 5 Experimental Results 37
5.1 Training Infrastructure 38
5.2 Classification Performance 38
5.2.1 Image Aumentation Validation Results 40
5.2.2 Test Results 41
5.2.3 Open Source Datasets Test Results 45
Chapter 6 Conclusion and Future Work 47
Reference 49
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