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研究生:王威淳
研究生(外文):Wei-Chun Wang
論文名稱:基於生成對抗網路(GAN)與Mask R-CNN之電腦輔助系統以增強大腸息肉偵測及分類
論文名稱(外文):Generative Adversarial Networks(GAN)and Mask R-CNN based Computer-Aided Detection and Diagnosis System to Enhance Colon Polyp Detection and Characterization
指導教授:張宏義
指導教授(外文):Chang, Hong-Yi
學位類別:碩士
校院名稱:國立嘉義大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2021
畢業學年度:110
語文別:中文
論文頁數:58
中文關鍵詞:大腸息肉偵測生成對抗網路(GAN,Generative Adversarial Network)物件偵測模型資料增強(Data Augmentation)去模糊化
外文關鍵詞:Colon Polyp DetectionGenerative Adversarial Network (GAN)Object Detection ModelData AugmentationDeblur
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大腸癌目前是全世界排名第三常見的癌症。大腸息肉為大腸癌之前身。所以大腸息肉的檢測為重要的臨床研究議題。如能及早發現大腸息肉,提前切除,就能有效降低死亡率。大腸鏡為偵測大腸息肉的首選方式。根據研究顯示,平均每人次完成大腸鏡檢仍有26% 的大腸息肉未被發現。因此利用深度學習來幫助醫師辨識出大腸息肉是目前重要的研究。大腸息肉又分為良性息肉與惡性息肉。惡性息肉中以扁平鋸齒狀腺瘤生長速度快,易惡變,但相對數量少且不易被發現。但訓練深度學習模型需要大量的訓練資料,因此本研究使用資料增強加上Conditional GAN生成出更多的扁平鋸齒狀腺瘤訓練資料集。再利用物件偵測模型YOLOv4訓練,以提供鏡檢醫師完善的電腦輔助偵測系統並提高腺瘤檢出率及準確的判斷息肉類別。此外大腸鏡即時操作時常會因晃動造成影像模糊而影響息肉辨識,所以本研究提出利用DeblurGAN-v2進行去模糊化。最後訓練Mask R-CNN,在偵測到息肉後,推算息肉的真實面積,提供鏡檢醫師一個可依照息肉大小判斷病情嚴重度及後續追蹤間隔時間的重要依據。經實驗結果後發現使用GAN資料比只使用資料擴增的模型的mAP提升了2.42%,對模糊圖片使用DeblurGAN-v2後再偵測的mAP提升5.10%,Mask R-CNN的mAP則為86.24%,可有效幫助醫師找出息肉並判斷病情嚴重度。
Colorectal cancer (CRC) is the third most common cancer in the world. Early detection and treatment can reduce mortality effectively. Colon polyp is the precursor of colon cancer. Colonoscopy is the “gold standard” for colon polyp detection. An average of 26% of miss rate after colonoscopy has been reported. Using deep learning algorithm to develop a computer-aided system for colon polyp detection is effective to reduce miss rate. Colon polyps are divided into neoplastic and non-neoplastic. Sessile serrated adenoma is one of the neoplastic polyp, which evolves to colon cancer in short duration, rare and not easy to detect. In order to train a reliable object detection model, a large database is needed. We used data augmentation technique and Conditional Generative Adversarial Networks to generate sessile serrated adenoma images with YOLOv4 to train the computer-aided detection and diagnosis model to improve polyp and adenoma detection rate. In addition, DeblurGAN-v2 was used to reduce the motion blurness during colonoscopy for detection accuracy enhancement. Lastly, a separate Mask R-CNN based model applies to the detected polyp for area estimation, which aids endoscopist to determine the risk of future CRC and surveillance interval. After the experimental results, it was found that the mAP of GAN data improved by 2.42% compared with the model using only data augmentation. The mAP of DeblurGAN-v2 for blurry images was improved by 5.10%. The mAP of Mask R-CNN is 86.24%, which can effectively help doctors to find polyps and determine the severity of the disease.
摘 要 i
目錄 iii
圖目錄 v
表目錄 vi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 2
第二章 文獻探討 4
第一節 CADe用於大腸息肉檢測 4
第二節 生成對抗網路GAN近期研究 10
第三節 去模糊化技術與DeblurGAN2 12
第四節 物件跟蹤技術 15
第三章 研究方法 17
第一節 GAN訓練流程 17
一、 調整圖片長寬 17
二、 標記資料 17
三、 使用Canny找出輪廓圖 18
四、 將輪廓圖覆蓋在ground truth圖上 19
五、 將覆蓋好的ground truth圖覆蓋在原圖上 19
六、 訓練Conditional GAN 20
七、 評估GAN訓練結果 24
(一) 峰值信噪比PSNR(Peak signal-to-noise ratio) 25
(二) 結構相似性SSIM(structural similarity) 25
第二節 YOLOv4訓練流程 26
一、 標記資料 26
二、 資料擴增 27
三、 訓練物件偵測模型 27
四、 模型指標比較 29
第三節 去模糊化測試資料 30
一、 對測試影片進行高斯模糊 30
二、 使用DeblurGAN-v2去模糊化 31
三、 評估去模糊化效果 31
第四節 bounding box大小比較和Deep SORT效果測試 31
第五節 息肉面積的估算 33
第四章 研究結果 36
第一節 GAN產生圖片比較 36
第二節 YOLOv4數據比較 38
第三節 YOLOv4數據不同資料 40
第四節 去模糊化比較 42
第五節 不同bounding box大小對準確度的影響 44
第六節 Deep SORT效果測試結果 46
第七節 Mask R-CNN數據與面積計算結果 47
第五章 結論 50
參考文獻 53
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