乳癌為女性中最為常見的癌症，主要由病理學家檢查病人之病理切片作為診斷方式。然而乳癌的人工診斷與分類十分的耗時，並且需要經驗豐富的病理學家來進行診斷，因此本研究將深度學習用於乳癌病理切片分割、良惡性組織辨識。

關於以深度學習進行乳癌的分割與組織辨識，大多數現有的研究著重於良性與惡性乳腺腫瘤的二元分類。而我們則是以四種不同生物標記(E-cadherin, P63, CK14與 CK5/6)的整張切片影像為依據，將乳癌分為正常乳腺組織、上皮增生、非典型乳腺導管癌、原位導管癌與浸潤性導管癌五個階段並在病理學家的監督下標註H\&E切片影像。我們以此由四種生物標記的切片為依據所標註出之整張H\&E切片影像來訓練深度學習卷積神經網絡(全卷積神經網路)，已達辨識良惡性組織並將乳癌組織分割的成果。

我們使用訓練完成的模型進行測試，最佳的模型測試的結果經過混淆矩陣量化分析後能夠在分割乳癌組織上達到準確率0.8768、精確率0.8477、召回率0.8037和綜合評價指標0.8137。在覆蓋率量化分析上，分割乳癌組織上達到覆蓋率50\%的比例為0.9248。這項研究結果顯示深度學習模型能夠達成自動化的乳癌影像分割與良惡性組織辨識，隨著更深入的研究，也可將其於臨床上使用，對於不論是學術研究或是醫療診斷都能夠有很大的貢獻。

Breast cancer, the most common cancer in women, is primarily diagnosed with inspection of histopathological slides by pathologists. Manual diagnosis and classification of breast cancer is time consuming and needs experienced pathologists,and thus this research use deep learning for segmentation of breast cancer histopathological image and recognition of benign or malignancy tissue.

About the detection and classification of breast cancer by Deep Learning. Most of the existing works focus only on binary classification of breast tumor (benign and malignant).We used whole slide images of four different biomarkers (E-cadherin, P63, CK14, and CK5/6) to categorize breast cancer stages into five different categories:normal breast tissue, Epithelial Hyperplasia, Atypical Ductal Carcinoma, Ductal Carcinoma Insitu and Invasive Ductal Carcinoma. Based on these four biomarkers we manually annotated H\&E slide under supervision of Pathologists. By using H\&E whole slide images which are annotated based on the four biomarkers, we trained the deep learning convolutional neural network (Fully Convolutional Network) to segment breast cancer tissue and recognize benign and malignancy tissue.

Our model was able to detect and classify different tissue of breast cancer with accuracy of 0.8768,precision of 0.8477,recall of 0.8037 and F-measure of 0.8137.In the evaluation of coverage ,the ratio of segmentation result which cover 50\% of the breast cancer tissue is 0.9248. These findings suggest that deep learning models can automate the process of segmentation and the recognition between benign and malignancy tissue and with further research it can be used for clinical use .

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