深度學習在生物醫學應用中的研究

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範白松(Pham Bach Tung) 查詢紙本館藏

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資訊工程學系

論文名稱

深度學習在生物醫學應用中的研究
(A Study on Deep Learning in Biomedical Applications)

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摘要(中)

深度學習已成為電腦視覺領域中高效且強大的工具，在醫學領域有重要的應用。本論文的重點在於介紹新穎的深度學習方法，用於心電圖（ECG）分類和皮膚病變分割。
在心電圖分類方面，我們徹底研究了一個新模型，其表現優於現有方法。令人印象深刻的是，它在PhysioNet數據集MIT-BIH上達到了98.5％的準確率，在PTB數據庫上達到了98.28％的準確率，並在PhysioNet Challenge 2017數據集上達到了約86.71％的F1分數。
在皮膚癌預測方面，傳統的皮膚科醫生視覺檢查面臨著準確率低、耗時長和依賴於人為因素和專業知識等限制。為了克服這些挑戰，我提出了一種新的輕量級分割模型，名為移動抗鋸齒注意力U-Net。實驗結果表明，對2018年皮膚影像合作（ISIC）和PH2數據集的我們的方法不僅需要更少的參數，而且在性能上優於幾種領先的分割方法。
總的來說，我們的深度學習方法在醫學應用中表現優越，由於其高效的推理能力，具有實際應用在真實設備上的潛力。本研究的成果有助於推動該領域的進步，為進一步研究和應用這些方法在醫學領域奠定基礎。

摘要(英)

Deep Learning has emerged as a highly efficient and powerful tool in the field of computer vision, with significant applications in the medical domain. This dissertation focuses on presenting novel Deep Learning approaches for electrocardiogram (ECG) classification and skin lesion segmentation.
For ECG classification, a new model was thoroughly investigated that outperforms existing methods. Impressively, it achieved an accuracy of 98.5% on the PhysioNet dataset MIT-BIH, 98.28% on the PTB database, and an F1 score of approximately 86.71% on the PhysioNet Challenge 2017 dataset.
In the context of skin cancer prediction, the conventional approach of visual examination by dermatologists faces its limitations, such as low accuracy, extensive time consumption, and reliance on human factors and expertise. To overcome these challenges, we propose a novel lightweight segmentation model, mobile anti-aliasing attention U-Net. Experimental results on the 2018 Skin Image Collaboration (ISIC) and PH2 datasets demonstrate that our approach not only requires fewer parameters, but also outperforms several state-of-the-art segmentation methods.
In summary, our Deep Learning approaches exhibit superiority in medical applications and hold the potential for practical implementation in real devices due to their efficient inference capabilities. The outcomes of this research contribute to the progress in this field and pave the way for further research and application of these methods in medical settings.

關鍵字(中)

★ 深度學習
★ 機器學習
★ 計算機視覺
★ 人工智能
★ 生物醫學
★ 分割
★ 心電圖

關鍵字(英)

★ Deep Learning
★ Machine Learning
★ Computer vision
★ Artificial Intelligence
★ Biomedical
★ Segmentation
★ Electrocardiogram

論文目次

Chinese Abstract i
English Abstract ii
Acknowledgements iii
Table of Content iv
List of Figures v
List of Tables vi
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Aim and Object 2
1.3 Thesis overview 3
Chapter 2 background knowledge 4
1. ECG classification 4
1.1. Description 4
1.2. ECG applications 4
2. Skin Lesion Segmentation 5
3. Loss functions and evaluation metrics 6
3.1. Loss functions 6
3.1.1. Binary Cross Entropy 6
3.1.2. Binary Focal Loss 7
3.1.3. Categorical Cross Entropy 7
3.2. Evaluation metrics 7
3.2.1. Classification metrics 7
3.2.2. Segmentation metrics 8
3.2.2.1 Dice coefficient 8
3.2.2.2 Jaccard coefficient 8
4. Components 8
4.1. Evolving Normalization-Activation Layers (EVO) 8
4.2. Squeeze-and-Excitation (SE) 9
4.3. Gradient Clipping 10
4.4. Anti-Aliasing Pooling 11
Chapter 3 electrocardiogram heartbeat classification on arrhythmias and myocardial infarction 12
1. Introduction 12
2. Related work 16
2.1. Convolutional Neural Network (CNN) 17
2.2. Recurrent Neural Network (RNN) 18
2.3. Long Short-Term Memory (LSTM) 20
3. Materials and Methods 21
3.1. Datasets 21
3.1.1. Pre-processing data 23
3.2. Proposed method 27
3.3. Evaluation Metrics 30
3.4. Loss function 30
3.4.1. Loss function For the PhysioNet′s MIT-BIH dataset 31
3.4.2. Loss function For the PhysioNet′s PTB dataset 31
3.4.3. Loss function For the PhysioNet Challenge 2017 dataset 31
3.5. Experiment Setup 31
3.5.1. The PhysioNet’s MIT-BIH dataset 31
3.5.2. The PhysioNet’s PTB dataset 32
3.5.3. PhysioNet Challenge 2017 dataset 32
3.5.4. Hyperparameters 32
3.5.5. Independent testing set 33
4. Results 34
4.1. The contribution of Evolving Normalization-Activation (EVO), Squeeze-and-Excitation (SE), and Gradient Clipping (GC) 34
4.2. The PhysioNet’s MIT-BIH Arrhythmias Classification 35
4.2.1. Comparative results 35
4.2.2. Confusion matrix 36
4.3. The PhysioNet’s PTB Myocardial Infarction classification 37
4.3.1. Comparative result 37
4.3.2. Confusion matrix 38
4.4. Atrial Fibrillation (AF) Anomaly Detection in PhysioNet Challenge 2017 dataset 39
5. Conclusions 40
Chapter 4 Anti-Aliasing Attention U-net Model for Skin Lesion Segmentation 41
1. Introduction 41
2. Related Works 47
3. Proposed Method 49
4. Experiments 53
4.1 Dataset 53
4.1.1 Dataset Modalities 53
4.1.2 The Preprocessing Dataset 54
4.2 Experimental Setups 55
4.3 The Evaluation Protocol 55
5 Results 57
5.1 Qualitative Results 57
6. Conclusions 64
Chapter 6 Conclusions and Future Works 65
References 67

參考文獻

[1] Antoni, L., Bruoth, E., Bugata, P., Bugata Jr, P., Gajdos, D., Horvát, S., Hudák, D., Kmecová, V., Stana, R., Stanková, M., Szabari, A., & Vozáriková, G. (2022). Automatic ECG classification and label quality in training data. Physiological Measurement, 43.
[2] Saini, S.K., & Gupta, R. (2021). Artificial intelligence methods for analysis of electrocardiogram signals for cardiac abnormalities: state-of-the-art and future challenges. Artificial Intelligence Review, 55, 1519-1565.
[3] Hu, R., Chen, J., & Zhou, L. (2022). A transformer-based deep neural network for arrhythmia detection using continuous ECG signals. Computers in biology and medicine, 144, 105325.
[4] Chen, J., Liao, K., Wei, K., Ying, H., Chen, D.Z., & Wu, J. (2022). ME-GAN: Learning Panoptic Electrocardio Representations for Multi-view ECG Synthesis Conditioned on Heart Diseases. International Conference on Machine Learning.
[5] Mohebbanaaz, Padma Sai, Y., & Rajani Kumari, L.V. (2020). A Review on Arrhythmia Classification Using ECG Signals. 2020 IEEE International Students′ Conference on Electrical, Electronics and Computer Science (SCEECS), 1-6.
[6] Mhamdi, L., Dammak, O., Cottin, F., & Dhaou, I.B. (2022). Artificial Intelligence for Cardiac Diseases Diagnosis and Prediction Using ECG Images on Embedded Systems. Biomedicines, 10.
[7] Yu, J., Park, S., Kwon, S., Cho, K., & Lee, H. (2022). AI-Based Stroke Disease Prediction System Using ECG and PPG Bio-Signals. IEEE Access, 10, 43623-43638.
[8] Ghosh, S.K., Tripathy, R.K., Paternina, M.R., Arrieta, J.J., Zamora-Méndez, A., & Naik, G.R. (2020). Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network. Journal of Medical Systems, 44, 1-15.
[9] Attallah, O. (2022). ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration. Computers in Biology and Medicine, 142, 105210 - 105210.
[10] Khan, A.H., Hussain, M., & Malik, M.K. (2021). ECG Images dataset of Cardiac and COVID-19 Patients. Data in Brief, 34.
[11] Wang, Y., Chen, L., Wang, J., He, X., Huang, F., Chen, J., & Yang, X. (2020). Electrocardiogram analysis of patients with different types of COVID‐19. Annals of Noninvasive Electrocardiology, 25.
[12] Sun, W., Kalmady, S.V., Sepehrvand, N., Chu, L.M., Wang, Z., Salimi, A., Hindle, A., Greiner, R., & Kaul, P. (2022). Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale. ArXiv, abs/2211.10431.
[13] Zagan, I., Gaitan, V.G., Iuga, N., & Brezulianu, A. (2018). m-GreenCARDIO embedded system designed for out-of-hospital cardiac patients. 2018 International Conference on Development and Application Systems (DAS), 11-17.
[14] Navaz, A.N., Mohammed, E., Serhani, M.A., & Zaki, N. (2016). The use of data mining techniques to predict mortality and length of stay in an ICU. 2016 12th International Conference on Innovations in Information Technology (IIT), 1-5.
[15] Steinhubl, S.R., Waalen, J., Edwards, A.M., Ariniello, L., Mehta, R.R., Ebner, G.S., Carter, C.T., Baca-Motes, K., Felicione, E., Sarich, T.C., & Topol, E.J. (2018). Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial. JAMA, 320, 146–155.
[16] Benhamida, A., Zouaoui, A., Szocska, G., Karóczkai, K., Slimani, G., & Kozlovszky, M. (2019). Problems in archiving long-term continuous ECG data – a review. 2019 IEEE 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI), 263-268.
[17] Guan, K., Shao, M., & Wu, S. (2017). A Remote Health Monitoring System for the Elderly Based on Smart Home Gateway. Journal of Healthcare Engineering, 2017.
[18] Rakovic, P., & Lutovac, B. (2015). A cloud computing architecture with wireless body area network for professional athletes health monitoring in sports organizations — Case study of Montenegro. 2015 4th Mediterranean Conference on Embedded Computing (MECO), 387-390.
[19] Octaviani, V., Kurniawan, A., Suprapto, Y.K., & Zaini, A. (2017). Alerting system for sport activity based on ECG signals using proportional integral derivative. 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 1-6.
[20] Lin, T., Goyal, P., Girshick, R.B., He, K., & Dollár, P. (2017). Focal Loss for Dense Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327.
[21] Liu, H., Brock, A., Simonyan, K., & Le, Q.V. (2020). Evolving Normalization-Activation Layers. ArXiv, abs/2004.02967.
[22] Hu, J., Shen, L., Albanie, S., Sun, G., & Wu, E. (2017). Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023.
[23] Zhang, J., He, T., Sra, S., & Jadbabaie, A. (2019). Why Gradient Clipping Accelerates Training: A Theoretical Justification for Adaptivity. arXiv: Optimization and Control.
[24] Zhang, R. (2019). Making Convolutional Networks Shift-Invariant Again. ArXiv, abs/1904.11486.
[25] Smith, S., Collins, A., Ferrari, R., Holmes, D.R., Løgstrup, S., McGhie, D.V., Ralston, J., Sacco, R.L., Stam, H.C., Taubert, K.A., Wood, D., & Zoghbi, W.A. (2012). Our time: a call to save preventable death from cardiovascular disease (heart disease and stroke). Circulation, 126 23, 2769-75.
[26] Hassan, M.F., Lai, D., & Bu, Y. (2019). Characterization of Single Lead Continuous ECG Recording with Various Dry Electrodes. Proceedings of the 2019 3rd International Conference on Computational Biology and Bioinformatics - ICCBB ′19.
[27] Scrugli, M.A., Loi, D., Raffo, L., & Meloni, P. (2019). A runtime-adaptive cognitive IoT node for healthcare monitoring. Proceedings of the 16th ACM International Conference on Computing Frontiers.
[28] Bousseljot, R., Kreiseler, D., & Schnabel, A. (2009). Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet.
[29] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C., & Stanley, H.E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101 23, E215-20.
[30] Clifford, G.D., Liu, C., Moody, B., Lehman, L.H., Silva, I., Li, Q., Johnson, A.E., & Mark, R.G. (2017). AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017. 2017 Computing in Cardiology (CinC), 1-4.
[31] Mohebbanaaz, Kumari, L.V., & Sai, Y.P. (2021). Classification of ECG beats using optimized decision tree and adaptive boosted optimized decision tree. Signal, Image and Video Processing, 16, 695 - 703.
[32] Ahmad, Z., Tabassum, A., Guan, L., & Khan, N.M. (2021). ECG Heartbeat Classification Using Multimodal Fusion. IEEE Access, 9, 100615-100626.
[33] Ye, C., Kumar, B.V., & Coimbra, M.T. (2012). Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals. IEEE Transactions on Biomedical Engineering, 59, 2930-2941.
[34] Pathoumvanh, S., Hamamoto, K., & Indahak, P. (2014). Arrhythmias detection and classification base on single beat ECG analysis. The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), 1-4.
[35] Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., & Adeli, H. (2017). Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Computers in biology and medicine, 100, 270-278.
[36] Rai, H.M., & Chatterjee, K. (2021). Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data. Applied Intelligence, 52, 5366 - 5384.
[37] Al-Zaiti, S.S., Besomi, L., Bouzid, Z., Faramand, Z., Frisch, S.O., Martin-Gill, C., Gregg, R.E., Saba, S.F., Callaway, C.W., & Sejdić, E. (2020). Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram. Nature Communications, 11.
[38] Yıldırım, Ö. (2018). A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification. Computers in biology and medicine, 96, 189-202.
[39] Saadatnejad, S., Oveisi, M.R., & Hashemi, M. (2018). LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices. IEEE Journal of Biomedical and Health Informatics, 24, 515-523.
[40] Yeh, L., Chen, W., Chan, H.Y., Lu, N., Wang, C., Twan, W., Du, W., Huang, Y., Hsu, S., & Chen, T. (2021). Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks. Biosensors, 11.
[41] Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60, 84 - 90.
[42] Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S.E., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1-9.
[43] Jain, P.K., Gajbhiye, P., Tripathy, R.K., & Acharya, U.R. (2020). A two-stage deep CNN architecture for the classification of low-risk and high-risk hypertension classes using multi-lead ECG signals. Informatics in Medicine Unlocked, 21, 100479.
[44] Jun, T.J., Nguyen, H.M., Kang, D., Kim, D., Kim, D., & Kim, Y. (2018). ECG arrhythmia classification using a 2-D convolutional neural network. ArXiv, abs/1804.06812.
[45] Anwar, S.M., Gul, M., Majid, M., & Alnowami, M.R. (2018). Arrhythmia Classification of ECG Signals Using Hybrid Features. Computational and Mathematical Methods in Medicine, 2018.
[46] Murat, F., Yıldırım, Ö., Talo, M., Baloglu, U.B., Demir, Y., & Acharya, U.R. (2020). Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review. Computers in biology and medicine, 120, 103726 .
[47] Ullah, H., Heyat, M.B., Akhtar, F., Muaad, A.Y., Ukwuoma, C.C., Bilal, M., Miraz, M.H., Bhuiyan, M.A., Wu, K., Damaševičius, R., Pan, T., Gao, M., Lin, Y., & Lai, D. (2022). An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal. Diagnostics, 13.
[48] Naz, M., Shah, J.H., Khan, M.A., Sharif, M., Raza, M., & Damaševičius, R. (2021). From ECG signals to images: a transformation based approach for deep learning. PeerJ Computer Science, 7.
[49] Cho, K., Merrienboer, B.V., Gülçehre, Ç., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. Conference on Empirical Methods in Natural Language Processing.
[50] Xiong, Z., Nash, M.P., Cheng, E., Fedorov, V.V., Stiles, M.K., & Zhao, J. (2018). ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network. Physiological Measurement, 39.
[51] Ganguly, B., Ghosal, A., Das, A., Das, D., Chatterjee, D., & Rakshit, D. (2020). Automated Detection and Classification of Arrhythmia From ECG Signals Using Feature-Induced Long Short-Term Memory Network. IEEE Sensors Letters, 4, 1-4.
[52] Hou, B., Yang, J., Wang, P., & Yan, R. (2020). LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification. IEEE Transactions on Instrumentation and Measurement, 69, 1232-1240.
[53] Kachuee, M., Fazeli, S., & Sarrafzadeh, M. (2018). ECG Heartbeat Classification: A Deep Transferable Representation. 2018 IEEE International Conference on Healthcare Informatics (ICHI), 443-444.
[54] Acharya, U.R., Oh, S.L., Hagiwara, Y., Tan, J.H., Adam, M., Gertych, A., & Tan, R.S. (2017). A deep convolutional neural network model to classify heartbeats. Computers in biology and medicine, 89, 389-396 .
[55] Martis, R.J., Acharya, U.R., Lim, C.M., Mandana, K.M., Ray, A.K., & Chakraborty, C. (2013). Application of Higher Order cumulant Features for Cardiac Health Diagnosis using ECG signals. International journal of neural systems, 23 4, 1350014 .
[56] Li, T., & Zhou, M. (2016). ECG Classification Using Wavelet Packet Entropy and Random Forests. Entropy, 18, 285.
[57] Safdarian, N., Dabanloo, N.J., & Attarodi, G. (2014). A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal. Journal of Biomedical Science and Engineering, 7, 818-824.
[58] Kojuri, J., Boostani, R., Dehghani, P., Nowroozipour, F., & Saki, N. (2015). Prediction of acute myocardial infarction with artificial neural networks in patients with nondiagnostic electrocardiogram. Journal of cardiovascular disease research, 6, 51-59.
[59] Sun, L., Lv, Y., Yang, K., & Li, S. (2012). ECG Analysis Using Multiple Instance Learning for Myocardial Infarction Detection. IEEE Transactions on Biomedical Engineering, 59, 3348-3356.
[60] Liu, B., Liu, J., Wang, G., Huang, K., Li, F., Zheng, Y., Luo, Y., & Zhou, F. (2015). A novel electrocardiogram parameterization algorithm and its application in myocardial infarction detection. Computers in biology and medicine, 61, 178-84.
[61] Sharma, L.N., Tripathy, R.K., & Dandapat, S. (2015). Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction. IEEE Transactions on Biomedical Engineering, 62, 1827-1837.
[62] Qiao, L., Xiao, C., Ma, T., Li, H., & Sun, J. (2019). MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals. International Joint Conference on Artificial Intelligence.
[63] Kohler, R.R. (1981). A segmentation system based on thresholding. Computer Graphics and Image Processing, 15, 319-338.
[64] Tayal, Y., Lamba, R., & Padhee, S. (2012). Automatic Face Detection Using Color Based Segmentation.
[65] Joshi, G.D., Sivaswamy, J., & Krishnadas, S.R. (2012). Depth Discontinuity-Based Cup Segmentation From Multiview Color Retinal Images. IEEE Transactions on Biomedical Engineering, 59, 1523-1531.
[66] Gould, S., Gao, T., & Koller, D. (2009). Region-based Segmentation and Object Detection. NIPS.
[67] Guth, F., & Campos, T.E. (2018). Skin lesion segmentation using U-Net and good training strategies. ArXiv, abs/1811.11314.
[68] Chen, L., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. ArXiv, abs/1706.05587.
[69] Lin, T., Dollár, P., Girshick, R.B., He, K., Hariharan, B., & Belongie, S.J. (2016). Feature Pyramid Networks for Object Detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 936-944.
[70] Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation. ArXiv, abs/1606.02147.
[71] Vuola, A.O., Akram, S.U., & Kannala, J. (2019). Mask-RCNN and U-Net Ensembled for Nuclei Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 208-212.
[72] Ma, Z., & Tavares, J.M. (2017). Effective features to classify skin lesions in dermoscopic images. Expert Syst. Appl., 84, 92-101.
[73] Jaisakthi, S.M., Mirunalini, P., & Aravindan, C. (2018). Automated skin lesion segmentation of dermoscopic images using GrabCut and k-means algorithms. IET Comput. Vis., 12, 1088-1095.
[74] Adjed, F., Gardezi, S.S., Ababsa, F., Faye, I., & Dass, S.C. (2017). Fusion of structural and textural features for melanoma recognition. IET Comput. Vis., 12, 185-195.
[75] Arroyo, J.L., & Garcia-Zapirain, B. (2019). Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding. Computer methods and programs in biomedicine, 168, 11-19.
[76] Do, T., Hoang, T., Pomponiu, V., Zhou, Y., Chen, Z., Cheung, N., Koh, D.C., Tan, A., & Tan, S.H. (2017). Accessible Melanoma Detection Using Smartphones and Mobile Image Analysis. IEEE Transactions on Multimedia, 20, 2849-2864.
[77] Alfed, N., & Khelifi, F. (2017). Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images. Expert Syst. Appl., 90, 101-110.
[78] Zhou, H., Li, X., Schaefer, G., Celebi, M.E., & Miller, P.C. (2013). Mean shift based gradient vector flow for image segmentation. Comput. Vis. Image Underst., 117, 1004-1016.
[79] Li, X., Chen, H., Qi, X., Dou, Q., Fu, C., & Heng, P. (2017). H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes. IEEE Transactions on Medical Imaging, 37, 2663-2674.
[80] Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y., & Wu, J. (2020). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1055-1059.
[81] Jha, D., Smedsrud, P.H., Riegler, M., Johansen, D., Lange, T.D., Halvorsen, P., & Johansen, H.D. (2019). ResUNet++: An Advanced Architecture for Medical Image Segmentation. 2019 IEEE International Symposium on Multimedia (ISM), 225-2255.
[82] Jha, D., Riegler, M., Johansen, D., Halvorsen, P., & Johansen, H.D. (2020). DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 558-564.
[83] VenkateshG, M., Naresh, Y.G., Little, S., & O’Connor, N.E. (2018). A Deep Residual Architecture for Skin Lesion Segmentation. OR 2.0/CARE/CLIP/ISIC@MICCAI.
[84] He, K., Gkioxari, G., Dollár, P., & Girshick, R.B. (2017). Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386-397.
[85] Badrinarayanan, V., Kendall, A., & Cipolla, R. (2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495.
[86] Geng, L., Zhang, S., Tong, J., & Xiao, Z. (2019). Lung segmentation method with dilated convolution based on VGG-16 network. Computer Assisted Surgery, 24, 27 - 33.
[87] Zhuang, J. (2018). LadderNet: Multi-path networks based on U-Net for medical image segmentation. ArXiv, abs/1810.07810.
[88] Punn, N.S., & Agarwal, S. (2020). Inception U-Net Architecture for Semantic Segmentation to Identify Nuclei in Microscopy Cell Images. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 16, 1 - 15.
[89] Le, P.T., Chang, C., Li, Y., Hsu, Y., & Wang, J. (2022). Antialiasing Attention Spatial Convolution Model for Skin Lesion Segmentation with Applications in the Medical IoT. Wireless Communications and Mobile Computing.
[90] Wang, J., Wei, L., Wang, L., Zhou, Q., Zhu, L., & Qin, J. (2021). Boundary-Aware Transformers for Skin Lesion Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention.
[91] Piccolo, D., Ferrari, A., Peris, K., Daidone, R., Ruggeri, B., & Chimenti, S. (2002). Dermoscopic diagnosis by a trained clinician vs. a clinician with minimal dermoscopy training vs. computer‐aided diagnosis of 341 pigmented skin lesions: a comparative study. British Journal of Dermatology, 147.
[92] Noel Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen Dusza, David Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael Marchetti, Harald Kittler, Allan Halpern: "Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)", 2018; https://arxiv.org/abs/1902.03368
[93] Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 doi:10.1038/sdata.2018.161 (2018).
[94] Codella N, Gutman D, Celebi ME, Helba B, Marchetti MA, Dusza S, Kalloo A, Liopyris K, Mishra N, Kittler H, Halpern A. "Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)". arXiv: 1710.05006
[95] Mendonça, T., Ferreira, P.M., Marques, J.S., Marçal, A.R., & Rozeira, J. (2013). PH2 - A dermoscopic image database for research and benchmarking. 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5437-5440.
[96] Ibtehaz, N., & Rahman, M.S. (2019). MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation. Neural networks : the official journal of the International Neural Network Society, 121, 74-87.
[97] Zafar, K., Gilani, S.O., Waris, A., Ahmed, A., Jamil, M., Khan, M.N., & Kashif, A.S. (2020). Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network. Sensors (Basel, Switzerland), 20.

指導教授

王家慶(Wang Jia-Ching)

審核日期

2023-7-19

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