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研究生：	楊濰澤 Yang, Wei-Tse
論文名稱：	利用遷移學習、卷積神經網路與具種族多樣性之影像資料集開發乳房X光攝影的全自動乳癌分類系統 Development of automatic breast cancer classification for mammography with convolutional neural network, transfer learning, and racially diverse datasets
指導教授：	方佑華 Fang, Yu-Hua
學位類別：	碩士 Master
系所名稱：	工學院 - 生物醫學工程學系 Department of BioMedical Engineering
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	68
中文關鍵詞：	深度學習、遷移學習、卷積神經網路、乳房X光線攝影、乳癌、召回率
外文關鍵詞：	Deep Learning, Convolutional Neural Network, Mammography, Transfer Learning, Breast Cancer, Recall Rate
相關次數：	點閱：106 下載：0
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乳癌是女性最常見的癌症。在台灣與亞洲，診斷的平均年齡比西方國家年輕10歲。為了檢測早期的乳癌，乳房X光線攝影是廣泛使用的影像造影方式。但是，篩檢有非常高的召回率。為了解決這個問題，幾十年來，研究試圖使用影像處理方法來建置電腦輔助診斷系統。最近，研究開始使用深度學習的方法。這些深度學習的研究在整張Ｘ光影像的分類上，已經展現出不錯的結果。然而，放射科醫生通常知道病變的位置，但他們在一些很難診斷的病例中，並不知道這些病例是不是癌症。因此，在我們的研究中，我們嘗試建構一個系統，該系統賴於手動圈選ROI而非整個圖像。此外，為了檢驗此模型是否可以降低高召回率，我們分析了模型在BI-RADS 3和4的表現。最後，我們檢測用西方數據訓練的模型是否可以應用於亞洲人口。

我們收集了三個公開數據集分別來自CBIS-DDSM，BCDR和INbreast，我們也從成大醫院收集了一個數據集。為了預測各種影像尺寸的ROI，我們提出了patch classification-based model和ROI pooling。Patch classification-based model是指CNN被延伸來處理更大影像。我們的方法可以處理四種不同大小的ROI。 ROI pooling可以處理任何尺寸的ROI。為了解決數據不平衡問題，我們調整了一個mini batch-size的數據組成，並我們也使用class weight。在研究中，patch classification-based model可以獲得整體73％的準確度，AUC也可以達到0.70以上。相比之下，ROI只能有61%的準確度。我們的研究發現，當陰性(negative)病例比陽性(positive)病例多20倍時，class weight只達到55％的準確率，但調整數據組成可達到約65％的準確率。當我們的模型與人類專家進行比較時，我們的實驗顯示專家在BI-RADS 3和4中僅具有50％的準確度，但我們的模型可以保持67％的準確度。此外，我們的模型在應用於成大醫院數據集時可以達到78％的準確率。我們的研究結果證明深度學習有很大的潛力來降低診斷的高召回率。此外，它也證明用西方數據集訓練的模型似乎可以適用於亞洲人口而無需任何再訓練。雖然我們能需要更多資料來驗證我們的模型，目前研究結果已經展現出深度學習在降低召回率以及應用在亞洲人口上有不錯的結果。

Breast Cancer is the most common cancer for women. In Taiwan and Asia countries, the average age of diagnosis is 10 years younger than that of western countries. To detect the breast cancer in the early stage, mammography is the most widely used modality. However, such screening usually results in a high recall rate or a high false positive rate. For decades, to solve this problem, researchers have tried to use image processing methods to build the CAD system. Recently, researchers have started to use methods of deep learning. The research of deep learning has shown promising results to build classifiers for whole X-ray images. Nonetheless, experts generally have known the location of the lesion, but it was difficult for them to diagnose some cases, especially in BI-RADS 3 and 4. Therefore, in our research, we tried to build the system that was dependent on the manual ROI extraction rather than whole images. Furthermore, to examine whether the model can reduce the high recall rate, we analyzed the performance of BI-RADS 3 and 4. Lastly, we examined whether the model trained with western data could be applied to the Asian population.

We collected three public datasets from CBIS-DDSM, BCDR, and INbreast and one private dataset from NCKU Hospital. To predict ROIs in various sizes, we adopted the patch classification-based model and ROI pooling. Patch classification-based model means the common convolutional neural networks was extended to process larger images. Our patch classification-based model can process ROIs in four different sizes. ROI pooling can process ROIs in any sizes. To solve the problem of data imbalance, we adjusted the data composition in one min-batch size and utilized the class weight. In patch classification-based model, our research result has shown the overall accuracy of 73.1% and achieved the AUC above 0.70 in any ROI sizes. Compared with patch classification-based model, ROI pooling only got the accuracy of 61%. In addition, our research found that when negative cases were 20 times more than positive cases, the class weight only achieved the accuracy of 55%, but the adjustment of data composition can achieve about 65%. When we compared with human experts, our experiment showed experts only possessed the accuracy of 50% in BI-RADS 3 and 4, but our models can maintain 67%. Moreover, our model can achieve the accuracy of 78% when it was applied to the dataset of NCKU Hospital. Our research results have shown that deep learning had the potential to reduce the high recall rate in clinics. Besides, it has demonstrated that the model trained with western dataset seemed to be applicable to Asian population without any fine-tuning. Although we still needed more clinical data to verify our results, our proposed model has shown promising results in the reduction of recall rate and the application of the Asian population.

Chapter 1 Overview	1
Chapter 2 Introduction	2
1	Breast Cancer	2
1.1	Mammography	3
1.2	The High Recall Rate	5
2	Computer Aided Diagnosis (CAD) System	7
2.1	The Benefit of the CAD System	7
2.2	Image Processing Technology	7
2.2.1	Texture Analysis	8
2.2.2	Detection for Masses	8
2.2.3	Detection for microcalcification	11
2.2.4	The Advantage of image processing technology	12
2.3	The Current CAD system	12
2.4	The CAD System on the Asian population	13
3	Deep Learning	15
3.1	Transfer Learning	16
3.2	Current Deep Learning Research on Mammography	18
3.2.1	Multiple-Stage Classification	19
3.2.2	Methods for Segmentation	20
3.2.3	Methods for Object Detection	21
3.2.4	Patch-Classification-Based Methods	22
4	Specific Aims	24
Chapter 3 Material and Methods	26
1	Data Acquisition	26
1.1	CBIS-DDSM	27
1.2	INbreast and Breast Cancer Digital Repository	27
1.3	National Cheng Kung University Hospital	27
2	Data Preprocessing and the Preparation for Training	30
2.1	Preprocessing	30
2.2	The Definition of the Size of Region of Interest	30
2.3	The Way to Split the Data	30
2.4	Data Augmentation	33
2.5	The Extraction from Background	33
3	Proposed Architecture	34
3.1	The Extension of the Convolutional Neural Network	36
3.1.1	Block Design	36
3.2	ROI Pooling	37
4	Data Generator	38
5	Imbalanced Classes	39
5.1	Class Weights	39
5.2	The Adjustment of Data Composition in One Mini-Batch Size	39
6	Training Detail	40
7	Li Shen’s Research	43
7.1	The Dataset	43
7.2	Data Augmentation	43
7.3	The Architecture of the Method	43
7.4	Training Detail	44
8	Operating Environment	45
Chapter 4 Results and Discussion	46
1	Class weights and the adjustment of data composition in one mini-batch size	46
2	Classification with 3 classes and 2 classes	47
3	The performance of each channel of the classification with 2 classes	49
4	The comparison between ROI pooling and patch classification-based model	52
5	The analysis with BI-RADS and CBIS-DDSM	53
6	The comparison between Shen’s research and my research	54
6.1	The number of iterations	55
6.2	The resizing	56
6.3	The bug in their program	56
7	The results from the data from NCKU Hospital	57
8	The Summary	59
8.1	Class Weight vs. The adjustment of data composition in one mini-batch size	59
8.2	The classification with 2class vs. 3 class	59
8.3	Patch classification-based model vs. ROI pooling	59
8.4	The deep learning model vs. Human experts	60
8.5	Li Shen’s research vs. My model	60
8.6	The performance of NCKU Hospital	60
9	Future works	61
Chapter 5 Conclusion	62
References	63

                                    

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