臺灣博碩士論文加值系統

本實驗使用Python語言編寫卷積神經網路（Convolutional Neural Network, CNN）建構生物組織偏振光學特性機器學習自動辨識系統。使用已經發展完成的混和調變式穆勒矩陣影像量測系統測量高分化肝細胞癌細胞與非腫瘤性肝細胞的切片組織，獲得細胞的穆勒矩陣影像及分解穆勒矩陣得到的細胞組織偏振特性影像，將其中可以觀察到差異性最明顯的偏振度影像用於深度學習訓練。最後利用迭代循環訓練及驗證獲得的模型來辨識生物細胞是否為癌變細胞，此方法相較於一般的人工辨識，僅需要使用訓練過後的模型，便能更快速且準確的對樣本進行分類，進而提升穆勒矩陣影像光學特性辨識之效率。

In this work, we constructing an automatic identification system (AIS) for the polarization optical properties of biological tissues based on machine learning technology with convolutional neural network (CNN) executed of Python language. We measure the biological tissue of hepatocellular carcinoma and non-neoplastic liver from a Muller matrix image system with hybrid modulation technique. Hybrid modulation technique has been developed for analysis the Muller matrix image of the cell, and decomposed Muller matrix to obtain the most obvious polarization images can be observed for deep learning training. Finally, the results obtained by iterative loop training and verification are used to distinguish whether biological cells are cancerous. Compared with general manual identification, this method only needs to use the trained model to classify samples more quickly and accurately, Thereby improve the efficiency of the recognition of the optical characteristics of the Muller matrix image.

誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VII
表目錄 X
第一章、 研究背景與目的 1
1.1研究背景 1
1.2 論文架構 5
第二章、 基本原理介紹 6
2.1 偏振光學表示方法 6
2.2 史托克參數與穆勒矩陣 8
2.2.1 偏振率 10
2.2.2穆勒矩陣 11
2.3 穆勒矩陣各項元素基本意義 13
2.3.1 衰減特性(Diattenuation effect) 14
2.3.2 偏振度(Polarizance) 15
第三章、 穆勒矩陣之極分解 18
3.1 Lu-Chipman極分解法(Polar decomposition) 18
3.1.1矩陣分解 19
3.1.2 衰減矩陣分解 20
3.1.3 退偏振能力 22
3.1.4 相位延遲係數 23
3.2 微分矩陣分解法(Differential decomposition) 25
3.2.1 微分矩陣參數意義 27
3.2.2 非退偏振與退偏振表示方法 29
3.2.3 相關光學參數解析方式 30
第四章、 深度學習系統 32
4.1神經網路與深度學習 32
4.2 卷積神經網路(Convolutional Neural Network, CNN) 40
第五章、 深度學習穆勒顯微系統架構 42
5.1主要光學元件特性與系統硬體架構 42
5.1.1 液晶相位延遲器基本特性 43
5.1.2 線性偏振片(Linear Polarizer)之穆勒矩陣與校正方式 44
5.1.3 偏振態產生器校正 46
5.2 穆勒矩陣量測方法 49
5.3 使用樣本偏振特性影像進行辨識的深度學習 52
第六章、 量測與訓練結果 57
6.1 肝細胞樣本 57
6.2 肝細胞樣本穆勒及分解 60
6.3 肝細胞偏振度影像深度學習結果 67
第七章、 結論與未來展望 70
第八章、 參考文獻 72

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