政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141617

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/141617

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題名:	基於卷積神經網路的多層次醫學影像檢索 Multi-hierarchy Medical Image Retrieval Based on Convolutional Neural Network
作者:	謝承曄 Hsieh, Cheng-Yeh
貢獻者:	羅崇銘 Lo, Chung-Ming 謝承曄 Hsieh, Cheng-Yeh
關鍵詞:	醫學影像基於內容的醫學影像檢索卷積神經網路多層次醫學影像分類分層結構的卷積神經網路 Medical image Content-based medical image retrieval Convolutional neural network Multi-hierarchy medical image Multi-level CNN
日期:	2022
上傳時間:	2022-09-02 15:00:04 (UTC+8)
摘要:	隨著醫學影像相關工具功能的增加與進步，醫學影像在醫院中廣泛地被使用。為了有效管理、檢索與利用醫學影像資料庫中的影像，基於內容的醫學影像檢索系統，能協助使用者尋找所需的資訊，且在醫學教育、臨床輔助診斷與研究領域上被應用。先前研究利用卷積神經網路(convolutional neural network, CNN)擷取影像特徵，並成功地建立醫學影像檢索系統，然而過去研究使用的資料量較少，且沒有呈現出醫學影像在臨床使用時，多種資訊與關聯性的呈現，除此之外，醫學影像中有許多是由一系列的 2D 連續切片影像組成，系列內影像皆十分相近，而先前研究沒有對此設計處理流程。因此本研究廣泛地從公開資料庫中搜集不同醫院產生的各式影像資料集，包括超過 10 個國家，數十個醫院、學校和實驗室的來源，並整理出 14 種成像模式，以及相對應的 40 種不同器官及 52 類不同疾病的多層次醫學影像資料庫，總共超過 50 萬張。實驗中按照成像模式、器官和疾病的層次結構進行分類，使用擷取代表性影像的方法來處理大量的資料，設計分層結構的卷積神經網路(Multi-level CNN)，在階層訓練中調整階層權重及參數的設計。由訓練完成的模型擷取特徵建立醫學影像檢索系統，檢索結果呈現同一系列不重複的 2D 切片影像，以提供更多元的檢索資訊。結果顯示擷取代表性影像的方法能夠減少 50%的訓練時間，同時提高平均檢索精準度 0.01。以此結合 Multi-level CNN 訓練分層結構的醫學影像資料庫，達到 0.86 的檢索精準度，高於文獻中使用 ResNet152 的 0.71。本研究提出的影像檢索架構能提升大規模醫學影像檢索系統的速度與精準度，以多層次影像結構呈現，協助使用者有效率地獲取欲查詢的影像資訊。 With the advancement of medical technology, medical imaging has been widely used in hospitals. To efficiently manage, retrieve and utilize the images in the medical image databases, the content-based medical image retrieval (CBMIR) systems can help users find the required information. CBMIR is widely used in the fields of medical education, clinical aided diagnosis, and research. Previous studies have used convolutional neural network (CNN) to extract image features and successfully build a medical image retrieval system. However, the amount of data used in previous studies is relatively small, and the presentation of various information and correlations in medical images has not been presented. In addition, many medical images consist of a series of 2D serial slices, which are very similar, and the processing flow has not been established in previous studies. Therefore, this study extensively collected various image datasets generated by different hospitals from public databases, including more than 10 countries, and dozens of sources of hospitals, schools, and laboratories. The dataset has a total of more than 500,000 images, including 14 imaging modalities, 40 organs, and 52 diseases, and experimental data are categorized by imaging modality, organ, and disease level. This study used 2 methods of capturing representative images to process large amounts of data. This study proposes multi-level convolutional neural network (Multi-level CNN) and adjusts layer weights and parameters during the training session. CBMIR system is established by extracting features from the trained model, and the retrieval results present the same series of non-repetitive 2D slice images to provide more diverse search information. The experimental results show that the method of capturing representative images can reduce the training time by 50% and improve the average retrieval accuracy by 0.01. Multi-level CNN combined with representative image methods achieves a retrieval accuracy of 0.86, which is higher than 0.71 using ResNet152 in the literature. The proposed image retrieval architecture can improve the speed and accuracy of large-scale medical image retrieval systems, which are presented in a multi-level image structure to help users efficiently obtain the desired image information.
參考文獻:	衛生福利部中央健康保險署. (2019). 全民健保資料庫應用服務. 衛生福利部 Retrieved from https://www.ey.gov.tw/File/536638F3EB20C262?A=C 衛生福利部中央健康保險署. (2020). 健保資料人工智慧應用研討會今盛大舉行，展現AI科技應用成果. Retrieved from https://www.nhi.gov.tw/News_Content.aspx?n=FC05EB85BD57C709&sms=587F1A3D9A03E2AD&s=A250E4E0D3A89837 Ahmad, J., Muhammad, K., Lee, M. Y., & Baik, S. W. (2017). Endoscopic image classification and retrieval using clustered convolutional features. Journal of medical systems, 41(12), 1-12. Akin, O., Elnajjar, P., Heller, M., Jarosz, R., Erickson, B., Kirk, S., & Filippini, J. (2016). Radiology data from the cancer genome atlas kidney renal clear cell carcinoma [TCGA-KIRC] collection. The Cancer Imaging Archive. Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020). Dataset of breast ultrasound images. Data in brief, 28, 104863. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval (Vol. 463). ACM press New York. Bafounta, M.-L., Beauchet, A., Aegerter, P., & Saiag, P. (2001). Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma?: Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Archives of dermatology, 137(10), 1343-1350. Baxter, G., & Anderson, D. (1995). Image indexing and retrieval: some problems and proposed solutions. New library world. Beichel, R., Ulrich, E., Bauer, C., Wahle, A., Brown, B., Chang, T., Plichta, K., Smith, B., Sunderland, J., & Braun, T. (2015). Data from qin-headneck. The Cancer Imaging Archive, 10, K9. Blanken, H. M., de Vries, A. P., Blok, H. E., & Feng, L. (2007). Multimedia retrieval. Springer. Bloch, B. Nicolas, Jain, Ashali, & Jaffe, & Carl, C. (2015). Data From BREAST-DIAGNOSIS. https://doi.org/http://doi.org/10.7937/K9/TCIA.2015.SDNRQXXR Born, J., Wiedemann, N., Brändle, G., Buhre, C., Rieck, B., & Borgwardt, K. (2020). Accelerating covid-19 differential diagnosis with explainable ultrasound image analysis. arXiv preprint arXiv:2009.06116. Bravo, A. A., Sheth, S. G., & Chopra, S. (2001). Liver biopsy. New England Journal of Medicine, 344(7), 495-500. Bruch, S., Wang, X., Bendersky, M., & Najork, M. (2019). An analysis of the softmax cross entropy loss for learning-to-rank with binary relevance. Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, Buda, M., Maki, A., & Mazurowski, M. A. (2018). A systematic study of the class imbalance problem in convolutional neural networks. Neural networks, 106, 249-259. Buzug, T. M. (2011). Computed Tomography. In R. Kramme, K.-P. Hoffmann, & R. S. Pozos (Eds.), Springer Handbook of Medical Technology (pp. 311-342). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-74658-4_16 Chen, W., Liu, Y., Wang, W., Bakker, E., Georgiou, T., Fieguth, P., Liu, L., & Lew, M. S. (2021). Deep image retrieval: A survey. arXiv preprint arXiv:2101.11282. Chowdhury, G. G. (2010). Introduction to modern information retrieval. Facet publishing. Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., & Pringle, M. (2013). The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. Journal of digital imaging, 26(6), 1045-1057. Codella, N. C., Gutman, D., Celebi, M. E., Helba, B., Marchetti, M. A., Dusza, S. W., Kalloo, A., Liopyris, K., Mishra, N., & Kittler, H. (2018). 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). 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018), Consortium, N. C. I. C. P. T. A. (2018). Radiology data from the clinical proteomic tumor analysis consortium pancreatic ductal adenocarcinoma (CPTAC-PDA) collection. The Cancer Imaging Archive 2018. In. CPTAC., N. C. I. C. P. T. A. C. (2018). Radiology Data from the Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma [CPTAC-UCEC] Collection [Data set]. The Cancer Imaging Archive. https://doi.org/https://doi.org/10.7937/k9/tcia.2018.3r3juisw da Silva Torres, R., & Falcao, A. X. (2006). Content-based image retrieval: theory and applications. RITA, 13(2), 161-185. Das, T. K., & Kumar, P. M. (2013). Big data analytics: A framework for unstructured data analysis. International Journal of Engineering Science & Technology, 5(1), 153. Datta, R., Joshi, D., Li, J., & Wang, J. Z. (2008). Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys (Csur), 40(2), 1-60. Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition, Doi, K. (2007). Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized medical imaging and graphics, 31(4-5), 198-211. Erickson, B., Kirk, S., Lee, Y., Bathe, O., Kearns, M., Gerdes, C., Rieger-Christ, K., & Lemmerman, J. (2016). Radiology data from the cancer genome atlas liver hepatocellular carcinoma [TCGA-LIHC] collection. Cancer Imaging Arch, 10, K9. Flask. Flask. Retrieved 6/27 from https://flask.palletsprojects.com/en/2.1.x/ Gao, L., Parker, K., Alam, S., & Lerner, R. (1995). Sonoelasticity imaging: theory and experimental verification. The Journal of the Acoustical Society of America, 97(6), 3875-3886. Goddi, A., Bonardi, M., & Alessi, S. (2012). Breast elastography: a literature review. Journal of ultrasound, 15(3), 192-198. Google. (2012). Google Zeitgest 2012. Retrieved 10/31 from https://archive.google.com/zeitgeist/2012/#the-world Google. (2020). Alphabet annual report 2020. https://abc.xyz/investor/static/pdf/2020_alphabet_annual_report.pdf?cache=8e972d2 Google. (2021). Google 搜尋的運作方式. Retrieved 11/09 from https://www.google.com/search/howsearchworks/ Gøtzsche, P. C., & Jørgensen, K. J. (2013). Screening for breast cancer with mammography. Cochrane Database of Systematic Reviews(6). Gray, H. (1878). Anatomy of the human body (Vol. 8). Lea & Febiger. Gudivada, V. N., & Raghavan, V. V. (1995). Content based image retrieval systems. Computer, 28(9), 18-22. Gueld, M. O., Kohnen, M., Keysers, D., Schubert, H., Wein, B. B., Bredno, J., & Lehmann, T. M. (2002). Quality of DICOM header information for image categorization. Medical imaging 2002: PACS and integrated medical information systems: design and evaluation, Gupta, A., & Ritu, G. (2020). SN-am Dataset: White Blood Cancer Dataset of B-All and Mm for Stain Normalization. In: Accessed: Feb. Hamreras, S., Benítez-Rochel, R., Boucheham, B., Molina-Cabello, M. A., & López-Rubio, E. (2019). Content based image retrieval by convolutional neural networks. International Work-Conference on the Interplay Between Natural and Artificial Computation, Harangi, B. (2018). Skin lesion classification with ensembles of deep convolutional neural networks. Journal of biomedical informatics, 86, 25-32. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition, Holback, C., Jarosz, R., Prior, F., Mutch, D., Bhosale, P., Garcia, K., & Erickson, B. (2016). Radiology data from the cancer genome atlas ovarian cancer [tcga-ov] collection. The Cancer Imaging Archive. Hu, H., Zheng, W., Zhang, X., Zhang, X., Liu, J., Hu, W., Duan, H., & Si, J. (2021). Content‐based gastric image retrieval using convolutional neural networks. International Journal of Imaging Systems and Technology, 31(1), 439-449. Huang, D., Swanson, E. A., Lin, C. P., Schuman, J. S., Stinson, W. G., Chang, W., Hee, M. R., Flotte, T., Gregory, K., & Puliafito, C. A. (1991). Optical coherence tomography. Science, 254(5035), 1178-1181. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. Proceedings of the IEEE conference on computer vision and pattern recognition, Humbetov, S. (2012). Data-intensive computing with map-reduce and hadoop. 2012 6th International Conference on Application of Information and Communication Technologies (AICT), Institute, T. N. C. (2021). 2021 The Cancer Imaging Archive (TCIA). Retrieved 10/31 from https://www.cancerimagingarchive.net/ InternetLiveStats. (2021). Google Search Statistics. InternetLiveStats. Retrieved 10/31 from https://www.internetlivestats.com/google-search-statistics/#ref-1 Jensen, J. A. (2007). Medical ultrasound imaging. Progress in biophysics and molecular biology, 93(1-3), 153-165. Kaggle. (2021). Kaggle. Retrieved 10/31 from https://www.kaggle.com/ Karim, R., Housden, R. J., Balasubramaniam, M., Chen, Z., Perry, D., Uddin, A., Al-Beyatti, Y., Palkhi, E., Acheampong, P., & Obom, S. (2013). Evaluation of current algorithms for segmentation of scar tissue from late gadolinium enhancement cardiovascular magnetic resonance of the left atrium: an open-access grand challenge. Journal of Cardiovascular Magnetic Resonance, 15(1), 1-17. Katz, J. J., & Fodor, J. A. (1963). The structure of a semantic theory. language, 39(2), 170-210. Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C., Liang, H., Baxter, S. L., McKeown, A., Yang, G., Wu, X., & Yan, F. (2018). Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell, 172(5), 1122-1131. e1129. Kinahan, P., Muzi, M., Bialecki, B., & Coombs, L. (2017). Data from ACRIN-FLT-Breast. https://doi.org/http://doi.org/10.7937/K9/TCIA.2017.ol20zmxg Kinahan, P., Muzi, M., Bialecki, B., & Coombs, L. (2018). Data from ACRIN-FMISO-Brain. https://doi.org/http://doi.org/10.7937/K9/TCIA.2018.vohlekok Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. Kiranyaz, S., Degerli, A., Hamid, T., Mazhar, R., Ahmed, R. E. F., Abouhasera, R., Zabihi, M., Malik, J., Hamila, R., & Gabbouj, M. (2020). Left ventricular wall motion estimation by active polynomials for acute myocardial infarction detection. IEEE Access, 8, 210301-210317. Kirk, S., Lee, Y., Kumar, P., Filippini, J., Albertina, B., Watson, M., & Lemmerman, J. (2016). Radiology data from the cancer genome atlas lung squamous cell carcinoma [TCGA-LUSC] collection. The Cancer Imaging Archive. Kirk, S., Lee, Y., Lucchesi, F., Aredes, N., Gruszauskas, N., Catto, J., & Lemmerman, J. (2016). Radiology data from the cancer genome atlas urothelial bladder carcinoma [TCGA-BLCA] collection. Cancer Imaging Arch, 96-108. Kirk, S., Lee, Y., Roche, C., Bonaccio, E., Filippini, J., & Jarosz, R. (2016). Radiology Data from The Cancer Genome Atlas Thyroid Cancer [TCGA-THCA] collection. The Cancer Imaging Archive. In. Kirk, S., Lee, Y., Sadow, C., & Levine, S. (2016). Radiology Data from The Cancer Genome Atlas Rectum Adenocarcinoma [TCGA-READ] collection. Cancer Imaging Arch. Kirk, S., Lee, Y., Sadow, C., Levine, S., Roche, C., Bonaccio, E., & Filiippini, J. (2016). Radiology data from the cancer genome atlas colon adenocarcinoma [TCGA-COAD] collection. The Cancer Imaging Archive, 10, K9. Kramme, R., Hoffmann, K.-P., & Pozos, R. S. (2011). Springer handbook of medical technology. Springer Science & Business Media. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, 1097-1105. Kubat, M., & Matwin, S. (1997). Addressing the curse of imbalanced training sets: one-sided selection. Icml, Kumar, A., Kim, J., Cai, W., Fulham, M., & Feng, D. (2013). Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. Journal of digital imaging, 26(6), 1025-1039. Lahiri, B., Bagavathiappan, S., Jayakumar, T., & Philip, J. (2012). Medical applications of infrared thermography: a review. Infrared Physics & Technology, 55(4), 221-235. Leavey, P., Sengupta, A., Rakheja, D., Daescu, O., Arunachalam, H., & Mishra, R. (2019). Osteosarcoma data from UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment [Data set]. The Cancer Imaging Archive. In. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017). A curated mammography data set for use in computer-aided detection and diagnosis research. Scientific data, 4(1), 1-9. Li, N., Li, T., Hu, C., Wang, K., & Kang, H. (2020). A benchmark of ocular disease intelligent recognition: One shot for multi-disease detection. International Symposium on Benchmarking, Measuring and Optimization, Li, X., Morgan, P. S., Ashburner, J., Smith, J., & Rorden, C. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. Journal of neuroscience methods, 264, 47-56. Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42, 60-88. Liu, L., Shen, F., Shen, Y., Liu, X., & Shao, L. (2017). Deep sketch hashing: Fast free-hand sketch-based image retrieval. Proceedings of the IEEE conference on computer vision and pattern recognition, Liu, Y., Zhang, D., Lu, G., & Ma, W.-Y. (2007). A survey of content-based image retrieval with high-level semantics. Pattern recognition, 40(1), 262-282. Lu, X., Wang, J., Li, X., Yang, M., & Zhang, X. (2018). An adaptive weight method for image retrieval based multi-feature fusion. Entropy, 20(8), 577. Lucchesi, F., & Aredes, N. Radiology Data from The Cancer Genome Atlas Stomach Adenocarcinoma [TCGA-STAD] collection, 2016. The Cancer Imaging Archive, 10, K9. Lucchesi, F., & Aredes, N. (2016). Radiology data from The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) collection. The Cancer Imaging Archive. DOI: https://doi. org/10.7937 K, 9. Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y., Baker, J. A., & Tourassi, G. D. (2008). Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural networks, 21(2-3), 427-436. Mildenberger, P., Eichelberg, M., & Martin, E. (2002). Introduction to the DICOM standard. European radiology, 12(4), 920-927. Müller, H., de Herrera, A. G. S., Kalpathy-Cramer, J., Demner-Fushman, D., Antani, S. K., & Eggel, I. (2012). Overview of the ImageCLEF 2012 medical image retrieval and classiFIcation tasks. CLEF (online working notes/labs/workshop), Müller, H., Kalpathy–Cramer, J., Caputo, B., Syeda-Mahmood, T., & Wang, F. (2009). Overview of the first workshop on medical content–based retrieval for clinical decision support at MICCAI 2009. MICCAI International Workshop on Medical Content-Based Retrieval for Clinical Decision Support, Müller, H., Michoux, N., Bandon, D., & Geissbuhler, A. (2004). A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. International journal of medical informatics, 73(1), 1-23. Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy). Cancer Imaging Arch, 10, 7937. Newitt, D., & Hylton, N. (2016). Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy. The Cancer Imaging Archive, 2. O`Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458. Ollinger, J. M., & Fessler, J. A. (1997). Positron-emission tomography. Ieee signal processing magazine, 14(1), 43-55. Ophir, J., Cespedes, I., Ponnekanti, H., Yazdi, Y., & Li, X. (1991). Elastography: a quantitative method for imaging the elasticity of biological tissues. Ultrasonic imaging, 13(2), 111-134. Owais, M., Arsalan, M., Choi, J., & Park, K. R. (2019). Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. Journal of clinical medicine, 8(4), 462. Pak, M., & Kim, S. (2017). A review of deep learning in image recognition. 2017 4th international conference on computer applications and information processing technology (CAIPT), Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., & Lerer, A. (2017). Automatic differentiation in pytorch. Pogorelov, K., Randel, K. R., Griwodz, C., Eskeland, S. L., de Lange, T., Johansen, D., Spampinato, C., Dang-Nguyen, D.-T., Lux, M., & Schmidt, P. T. (2017). Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection. Proceedings of the 8th ACM on Multimedia Systems Conference, Qayyum, A., Anwar, S. M., Awais, M., & Majid, M. (2017). Medical image retrieval using deep convolutional neural network. Neurocomputing, 266, 8-20. Rajendrakumar Gare, G., Tran, H. V., deBoisblanc, B. P., Rodriguez, R. L., & Galeotti, J. M. (2022). Weakly Supervised Contrastive Learning for Better Severity Scoring of Lung Ultrasound. arXiv e-prints, arXiv: 2201.07357. Resmini, R., da Silva, L. F., Medeiros, P. R., Araujo, A. S., Muchaluat-Saade, D. C., & Conci, A. (2021). A hybrid methodology for breast screening and cancer diagnosis using thermography. Computers in Biology and Medicine, 135, 104553. Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), 386. Rosenblatt, F. (1961). Principles of neurodynamics. perceptrons and the theory of brain mechanisms. Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747. Rui, Y., Huang, T. S., & Chang, S.-F. (1999). Image retrieval: Past, present, and future. Journal of Visual Communication and Image Representation, 10(1), 1-23. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., & Bernstein, M. (2015). Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3), 211-252. Salz, D. A., & Witkin, A. J. (2015). Imaging in diabetic retinopathy. Middle East African journal of ophthalmology, 22(2), 145. Sanderson, M., & Croft, W. B. (2012). The history of information retrieval research. Proceedings of the IEEE, 100(Special Centennial Issue), 1444-1451. Sawka, M. N., Cheuvront, S. N., & Carter, R. (2005). Human water needs. Nutrition reviews, 63(suppl_1), S30-S39. Scott, M. L., & SCOTT, M. L. (1998). Dewey decimal classification. Libraries Unlimited. Shibata, N., Tanito, M., Mitsuhashi, K., Fujino, Y., Matsuura, M., Murata, H., & Asaoka, R. (2018). Development of a deep residual learning algorithm to screen for glaucoma from fundus photography. Scientific reports, 8(1), 1-9. Singh, P., Singh, S., & Kaur, G. (2008). A study of Gaps in CBMIR using different methods and prospective. Proceedings of world academy of science, engineering and technology, Srivastava, N., & Salakhutdinov, R. R. (2013). Discriminative transfer learning with tree-based priors. Advances in neural information processing systems, 26. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition, Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition, Townsend, D. W. (2008). Positron emission tomography/computed tomography. Seminars in nuclear medicine, Tuceryan, M., & Jain, A. K. (1993). Texture analysis. Handbook of pattern recognition and computer vision, 235-276. Wan, J., Wang, D., Hoi, S. C. H., Wu, P., Zhu, J., Zhang, Y., & Li, J. (2014). Deep learning for content-based image retrieval: A comprehensive study. Proceedings of the 22nd ACM international conference on Multimedia, Werbos, P. J. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 1550-1560. Wu, Z., Ke, Q., Sun, J., & Shum, H.-Y. (2011). Scalable face image retrieval with identity-based quantization and multireference reranking. IEEE transactions on pattern analysis and machine intelligence, 33(10), 1991-2001. Wunderling, T., Golla, B., Poudel, P., Arens, C., Friebe, M., & Hansen, C. (2017). Comparison of thyroid segmentation techniques for 3D ultrasound. Medical Imaging 2017: Image Processing, Xia, P., Zhang, L., & Li, F. (2015). Learning similarity with cosine similarity ensemble. Information Sciences, 307, 39-52. Yasmin, M., Mohsin, S., & Sharif, M. (2014). Intelligent image retrieval techniques: a survey. Journal of applied research and technology, 12(1), 87-103. Zhu, X., & Bain, M. (2017). B-CNN: branch convolutional neural network for hierarchical classification. arXiv preprint arXiv:1709.09890. Zuley, M., Jarosz, R., Drake, B., Rancilio, D., Klim, A., Rieger-Christ, K., & Lemmerman, J. (2016). Radiology data from the cancer genome atlas prostate adenocarcinoma [tcga-prad] collection. Cancer Imaging Arch, 9. Zysk, A. M., Nguyen, F. T., Oldenburg, A. L., Marks, D. L., & Boppart, S. A. (2007). Optical coherence tomography: a review of clinical development from bench to bedside. Journal of biomedical optics, 12(5), 051403.
描述:	碩士國立政治大學圖書資訊與檔案學研究所 109155017
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0109155017
資料類型:	thesis
DOI:	10.6814/NCCU202201220
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