臺灣博碩士論文加值系統

U-Net架構常用於醫學影像分割，主要好處是簡單、容易構建且適用於不同尺度的影像。醫學影像內容比較固定且單一，所有特徵都是重要的，不適合刪減資訊，而傳統U-Net架構的編碼區塊過於簡單，在乾癬的特徵學習較沒有效率且分割能力不佳。為了改善語意分割能力，本論文改進編碼區塊與解碼區塊，當中也增加注意力模塊以加強資訊的結合。使用修改的架構分割的病例影像，在乾癬分割的並交比（Intersection over Union, IOU）提升近10%，可用於計算身體各部位乾癬與皮膚的像素點占比，再依據體表面積(Body Surface Area, BSA)計算嚴重度。

The U-Net architecture is often used for medical image segmentation. The main advantage is that it is simple, easy to construct and suitable for images of different scales. The medical image content is relatively fixed and single, all features are important, and it is not suitable for deleting information. The coding block of the traditional U-Net architecture is too simple, and the feature learning in psoriasis is less efficient and the segmentation ability is poor. In order to improve the semantic segmentation ability, this paper improves the coding block and the decoding block, and also adds a attention module to strengthen the combination of information. Case images segmented using the modified architecture have increased the cross-over ratio of psoriasis segmentation by nearly 10%, which can be used to calculate the proportion of psoriasis and skin pixels in various parts of the body, and then calculate the severity based on the Body Surface Area.

摘 要 I
ABSTRACT II
目錄 IV
圖目錄 V
表目錄 VI
第一章、 諸論 1
1.1 前言 1
1.2 研究動機與目的 1
1.3 論文結構 2
第二章、 相關文獻回顧 3
2.1 與乾癬相關背景的論文 3
2.2 與研究主題有關的論文 3
2.3 與注意力卷積模塊(CONVOLUTIONAL BLOCK ATTENTION MODULE, CBAM)架構有關的論文 3
2.4 與空洞卷積(ATROUS CONVOLUTION)架構有關的論文 4
2.5 與U-NET方法有關的論文 5
2.6 其他相關架構的論文 6
第三章、 研究方法 7
3.1 研究過程 7
3.2 資料前處理與設備說明 9
3.3 理論基礎 11
3.4 方法架構 19
第四章、 實驗結果 22
4.1 評估方法 22
4.2 訓練過程比較 24
4.3 成果數據比較評估 25
4.4 成果影像比對評估 27
第五章、 結論 29
5.1 結論 29
5.2 研究限制 29

[1] Siddhartha Dutta, Shalini Chawla, and Sahil Kumar. "Psoriasis: A review of existing therapies and recent advances in treatment." J. Ration. Pharmacother Res Volume 4, 2018.
[2] Marks R, Barton SP, Shuttleworth D, Finlay AY. "Assessment of disease progress in psoriasis." Archives of Dermatology, vol. 125, no. 2, pp. 235-240, 1989.
[3] Jin-Shiuh Taur. "Neuro-fuzzy approach to the segmentation of psoriasis images." The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 35, pp. 19-27, 2003.
[4] Ridhi Arora, Balasubramanian Raman, Kritagya Nayyar, Ruchi Awasthi. " Automated skin lesion segmentation using attention-based deep convolutional neural network." Biomedical Signal Processing and Control, vol. 65, 2021.
[5] Ozan Oktay, Jo Schlemper, Loic Le Folgoc, Matthew Lee, Mattias Heinrich, Kazunari Misawa, Kensaku Mori, Steven McDonagh, Nils Y Hammerla, Bernhard Kainz, Ben Glocker, Daniel Rueckert. "Attention u-net: Learning where to look for the pancreas." arXiv preprint arXiv:1804.03999, 2018.
[6] Ashish Sinha, Jose Dolz. "Multi-scale self-guided attention for medical image segmentation. " IEEE Journal of Biomedical and Health Informatics, 2020.
[7] Jun Fu, Jing Liu, Jie Jiang, Yong Li, Yongjun Bao, Hanqing Lu. "Scene segmentation with dual relation-aware attention network." IEEE Transactions on Neural Networks and Learning Systems , 2020.
[8] Fisher Yu, Vladlen Koltun. "Multi-scale context aggregation by dilated convolutions." arXiv preprint arXiv:1511.07122, 2015.
[9] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille. "Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs." IEEE transactions on pattern analysis and machine intelligence 40.4, pp. 834-848, 2017.
[10] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam. "Encoder-decoder with atrous separable convolution for semantic image segmentation." Proceedings of the European conference on computer vision (ECCV). 2018.
[11] Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison Cottrell. "Understanding convolution for semantic segmentation." 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, 2018.
[12] Olaf Ronneberger, Philipp Fischer, Thomas Brox. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.
[13] Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh, Jianming Liang. "Unet++: A nested u-net architecture for medical image segmentation." Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer, Cham, 2018.
[14] Huimin Huang, Lanfen Lin, Ruofeng Tong, Hongjie Hu, Qiaowei Zhang, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, Jian Wu. "Unet 3+: A full-scale connected unet for medical image segmentation." ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020.
[15] Augustus Odena, Vincent Dumoulin, Chris Olah. "Deconvolution and checkerboard artifacts." Distill, 2016.
[16] François Chollet. "Xception: Deep learning with depthwise separable convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
[17] Jonathan Long, Evan Shelhamer, Trevor Darrell. "Fully convolutional networks for semantic segmentation." Proceedings of the IEEE conference on computer vision and pattern recognition, 2015.