基於資料擴增、不確定性標籤處理與集成學習之胸腔疾病分類

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研究生：	劉員宏 Yuan-Hung Liu
論文名稱：	基於資料擴增、不確定性標籤處理與集成學習之胸腔疾病分類 Thorax Symptom Classification Based on Data Augmentation, Uncertainty Label Handling, and Multi-model Ensemble
指導教授：	林昌鴻 Chang-Hong Lin
口試委員:	林昌鴻 Chang-Hong Lin 陳永耀林敬舜 Ching-Shun Lin 王煥宗 Huan-Chun Wang 陳維美 Wei-Mei Chen
學位類別：	碩士 Master
系所名稱：	電資學院 - 電子工程系 Department of Electronic and Computer Engineering
論文出版年：	2020
畢業學年度：	108
語文別：	英文
論文頁數：	85
中文關鍵詞：	醫學影像分類、不確定性標籤處理、深度學習、卷積神經網路 (CNN) 、資料擴增、集成學習、加權損失函數
外文關鍵詞：	Medical image Classification, Uncertainty Label Handling, Deep Learning, Convolution Neural Networks (CNNs), Data Augmentation, Model Ensemble, Weighted loss
相關次數：	點閱：232 下載：0
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上一筆

進年來，深度學習技術被廣泛得應用在影像處理上。其中一個熱門的應用是醫學影像分類處理。胸腔X光攝影是一個常見且有不錯品質的胸腔疾病診斷方式。然而，訓練一個合格的放射科醫師需要很長的時間，而且，即便是專業的放射科醫師依然會有診斷錯誤的時候。因此，人們有了使用電腦輔助診斷的想法。因為在胸腔X光影像的資料集當中，胸腔在照片中佔有的比例、身體的旋轉及位移是很常見的，另外，不確定標籤也是另一種醫學資料集中常見的問題。針對這些問題，我們在此篇論文中提出了一些方法。針對不確定標籤，我們使用了不確定性標籤處理方法。為了增加資料的多樣性及模擬一些胸腔X光影像資料集中常見的情況，我們提出了一些資料擴增的方法。另外，為了進一步提升，我們使用了集成學習及加權損失函數。在我們的實驗中，我們使用CheXpert 資料集，在三種流行的卷積網路架構平均上，我們的最好模型獲得了0.880 的AUC 5 ，並相較於沒有使用任何方法的模型提高了0.030。

The deep learning technique has been widely used for image processing recently. One of the most popular subjects is medical image classification. The chest radiograph is the most common way to diagnose thorax disease and had good success. However, training a qualified radiologist needs a long time and the diagnosis still exists mistakes. Computer-aided diagnosis, therefore, has been proposed. Since we observed that within existing chest X-ray image datasets, it is prevalent that images have different chest occupancy ratios, and the bodies might be biased or rotated. Moreover, the uncertain labels are also common in the datasets. In this thesis, we proposed some methods to alleviate these problems. The uncertainty labels handling strategies are used to handle the uncertainty labels, and the data augmentation strategies are used to increase the data diversity and simulate different chest occupancy ratios, rotated and biased bodies in the chest X-ray datasets. For further improvement, we applied model ensemble and weighted loss to improved the accuracy of diagnosis. In the experiment, on the CheXpert dataset, by using the proposed methods, on average of three popular architectures, the best model score an AUC 5 of 0.880, which is 0.030 better than the model without any modification.

摘要    I
ABSTRACT    II
致謝    III
LIST OF CONTENTS    IV
LIST OF FIGURES    VI
LIST OF TABLES    VIII
CHAPTER 1    INTRODUCTIONS    1
1    Motivation    1
2    Contributions    2
3    Thesis Organization    3
CHAPTER 2    RELATED WORKS    4
1    Chest X-ray datasets    4
2    Architectures    8
2.1    DenseNet 121 [2]    8
2.2    Inception-ResNet-v2 [18]    11
2.3    Xception [1]    17
CHAPTER 3    PROPOSED METHODS    20
1    Preprocessing    21
1.1    Uncertainty Label Handling    22
1.2    Data Augmentation    24
1.2.1    Expand and Shrink    24
1.2.2    Rotate    27
1.2.3    Shift    29
1.3    Image Cropping    31
2    Optimization    32
3    Ensemble Strategy    34
CHAPTER 4    EXPERIMENTAL RESULTS    36
1    Experimental Environment    36
2    Prediction    37
3    Evaluation    39
4    Experimental Results    40
4.1    Comparing with smooth-U-ONES and smooth-U-ZEROS strategy    41
4.2    Comparing with training different parts of the image    43
4.3    Results of ensemble models    48
4.4    Results of Weighted-BCE models    53
4.5    Prediction of the models    58
CHAPTER 5    CONCLUSIONS and Future works    69
1    Conclusions    69
2    Future Works    70
REFERENCES    71

                                

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全文公開日期 2025/08/10 (校內網路)
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