Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/157234
|
| Title: | 基於注意力機制的多級別醫學影像近鄰檢索
Multi-level Medical Imaging Neighbor Retrieval Based on Attention Mechanism |
| Authors: | 顏劭宇
Yan, Shou-Yu |
| Contributors: | 羅崇銘
Lo, Chung-Ming
顏劭宇
Yan,Shou-Yu |
| Keywords: | 醫學影像
基於內容的醫學影像檢索
注意力機制
多級別醫學影像分類
近鄰檢索
Medical Imaging
Content-Based Medical Image Retrieval
Attention Mechanism
Multi-Level Medical Image Classification
Nearest Neighbor Retrieval |
| Date: | 2025 |
| Issue Date: | 2025-06-02 14:48:50 (UTC+8) |
| Abstract: | 醫學影像是現代醫療中不可或缺的元素，藉由醫學影像中提供的身體狀態資訊能夠進行更準確的診斷、篩檢、治療規畫、治療反應和預後的評估。醫學影像資料庫內蘊含了豐富的資訊和潛在的未發現知識，其中包含了來自各種設備醫學影像，透過影像檢索，可以從資料庫中選取與查詢目標相關的影像，用於知識學習與資料探勘。
本研究從公開平台收集了53萬張醫學影像，涵蓋14種成像模式、40種器官及52種疾病，並依據成像模式、器官及疾病分為三個級別。實驗中採用了DenseNet、ConvNeXt、Vision Transforme(ViT)與Swin Transformer(Swin)等深度學習網路，並結合Hierarchical Classification(HC)進行多級別特徵學習。為提升分類準確度，研究中引入Cross Spatial and Channel Attention(CSCA)與Level-Aware Attention(LAA)，以強化對通道、空間及跨級別特徵的學習能力。結果顯示，ConvNeXt結合HC+LAA在分類任務中達到最高準確率97.25%，而Swin結合HC+CSCA+LAA的準確率為95.84%
在檢索階段，除全量檢索外，亦採用了HNSW(Hierarchical navigable small world)、SOAR (Spilling with Orthogonality-Amplified Residuals)與本研究提出的CCDP (Category-center distance pre-screening)三種近鄰檢索方法，以加快檢索速度。實驗結果顯示，ConvNeXt結合HC+CSCA在全量檢索中的mAP值達0.98；Swin結合HC+LAA在全量檢索中的mAP值為0.89。在近鄰檢索方法中，ConvNeXt結合HC+LAA的mAP在SOAR與CCDP中均達0.81，而HNSW中稍降至0.80。Swin結合HC+LAA在HNSW、SOAR與CCDP檢索中均達0.86。在建構時間與檢索時間方面，CCDP的索引建構時間僅11秒，大幅小於HNSW的10分鐘與SOAR的1分鐘。在查詢階段，CCDP之平均檢索時間為2秒，大幅快於全量檢索的23小時、HNSW的5秒與SOAR的20秒。綜合來看，本研究結合多級別分類與多重注意力機制，加上CCDP 近鄰檢索方式，能夠在兼顧精準度的同時大幅降低檢索時間。
Medical imaging is an indispensable component of modern healthcare. By providing information about the body’s condition, medical imaging enables more accurate diagnoses, screenings, treatment planning, evaluation of treatment responses, and prognostic assessments. Medical imaging databases contain rich information and potential undiscovered knowledge, including images from various devices. Through image retrieval techniques, relevant images related to specific queries can be selected from the database for knowledge learning and data mining.
In this study, we collected 530,000 medical images from the public TCIA and Kaggle platforms, encompassing 14 imaging modalities, 40 organs, and 52 diseases. These data were organized into three hierarchical levels based on modality, organ, and disease. We employed deep learning architectures—including DenseNet, ConvNeXt, Vision Transformer (ViT), and Swin Transformer (Swin)—in conjunction with Hierarchical Classification (HC) to learn multi-level features. To further enhance classification accuracy, we introduced Cross-Spatial and Channel Attention (CSCA) and Level-Aware Attention (LAA) mechanisms, strengthening the learning of channel-wise, spatial, and cross-level features. The results show that ConvNeXt combined with HC+LAA achieved the highest classification accuracy of 97.25%, while Swin with HC+CSCA+LAA achieved 95.84%.
During the retrieval phase, in addition to exhaustive search, we adopted three approximate nearest-neighbor methods—HNSW (Hierarchical Navigable Small World), SOAR (Spilling with Orthogonality-Amplified Residuals), and CCDP (Category-Center Distance Pre-Screening)—to accelerate retrieval speed. Under exhaustive search, ConvNeXt with HC+CSCA attained an mAP of 0.98, whereas Swin with HC+LAA achieved an mAP of 0.89. For the approximate methods, ConvNeXt with HC+LAA achieved an mAP of 0.81 with both SOAR and CCDP, and 0.80 with HNSW. Swin with HC+LAA consistently reached an mAP of 0.86 across HNSW, SOAR, and CCDP. In terms of index construction and query times, CCDP’s index built in only 11 seconds—dramatically faster than HNSW’s 10 minutes and SOAR’s 1 minute—and its average query time was 2 seconds, compared to 23 hours for exhaustive search, 5 seconds for HNSW, and 20 seconds for SOAR. Overall, by combining hierarchical classification with multiple attention mechanisms and employing the CCDP retrieval strategy, our approach achieves high accuracy while dramatically reducing retrieval time. |
| Reference: | 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.
Barhoumi, W., & Khelifa, A. (2021). Skin lesion image retrieval using transfer learning-based approach for query-driven distance recommendation. Computers in biology and medicine, 137, 104825.
Beck, T., Zhang, N., Shah, A., Khoncarly, S., McHenry, C., & Jin, J. (2021). Thyroid cancer identified after positron emission tomography (PET) shows aggressive histopathology. Journal of Surgical Research, 260, 245-250.
Benson, A. B., D’Angelica, M. I., Abbott, D. E., Anaya, D. A., Anders, R., Are, C., Bachini, M., Borad, M., Brown, D., & Burgoyne, A. (2021). Hepatobiliary cancers, version 2.2021, NCCN clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network, 19(5), 541-565.
Benson, A. B., Venook, A. P., Al-Hawary, M. M., Azad, N., Chen, Y.-J., Ciombor, K. K., Cohen, S., Cooper, H. S., Deming, D., & Garrido-Laguna, I. (2022). Rectal cancer, version 2.2022, NCCN clinical practice guidelines in oncology. Journal of the National Comprehensive Cancer Network, 20(10), 1139-1167.
Bevers, T. B., Helvie, M., Bonaccio, E., Calhoun, K. E., Daly, M. B., Farrar, W. B., Garber, J. E., Gray, R., Greenberg, C. C., Greenup, R., Hansen, N. M., Harris, R. E., Heerdt, A. S., Helsten, T., Hodgkiss, L., Hoyt, T. L., Huff, J. G., Jacobs, L., Lehman, C. D., . . . Kumar, R. (2018). Breast Cancer Screening and Diagnosis, Version 3.2018, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 16(11), 1362-1389. https://doi.org/10.6004/jnccn.2018.0083
Birjandi, S. M., & Khasteh, S. H. (2021). A survey on data mining techniques used in medicine. Journal of diabetes & metabolic disorders, 20(2), 2055-2071.
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
Bonjoc, K.-J., Young, H., Warner, S., Gernon, T., Maghami, E., & Chaudhry, A. (2020). Thyroid cancer diagnosis in the era of precision imaging. Journal of Thoracic Disease, 12(9), 5128.
Chen, Y., Tang, Y., Huang, J., & Xiong, S. (2023). Multi-scale triplet hashing for medical image retrieval. Computers in biology and medicine, 155, 106633.
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.
Deda, L. C., Goldberg, R. H., Jamerson, T. A., Lee, I., & Tejasvi, T. (2022). Dermoscopy practice guidelines for use in telemedicine. NPJ Digit Med, 5(1), 55. https://doi.org/10.1038/s41746-022-00587-9
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., & Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Drake, L. R., Hillmer, A. T., & Cai, Z. (2020). Approaches to PET imaging of glioblastoma. Molecules, 25(3), 568.
Dubey, S. R. (2021). A decade survey of content based image retrieval using deep learning. IEEE Transactions on Circuits and Systems for Video Technology, 32(5), 2687-2704.
Eaton, B. R., Schwarz, R., Vatner, R., Yeh, B., Claude, L., Indelicato, D. J., & Laack, N. (2021). Osteosarcoma. Pediatric blood & cancer, 68, e28352.
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.
Flaxel, C. J., Adelman, R. A., Bailey, S. T., Fawzi, A., Lim, J. I., Vemulakonda, G. A., & Ying, G. S. (2020). Diabetic Retinopathy Preferred Practice Pattern®. Ophthalmology, 127(1), P66-p145. https://doi.org/10.1016/j.ophtha.2019.09.025
Frățilă, S., & Rotaru, M. (2021). Clinical‑dermoscopic similarities between atypical nevi and early stage melanoma. Experimental and Therapeutic Medicine, 22(2), 1-5.
Gal, R., Van Velzen, S. G., Hooning, M. J., Emaus, M. J., Van Der Leij, F., Gregorowitsch, M. L., Blezer, E. L., Gernaat, S. A., Lessmann, N., & Sattler, M. G. (2021). Identification of risk of cardiovascular disease by automatic quantification of coronary artery calcifications on radiotherapy planning CT scans in patients with breast cancer. JAMA oncology, 7(7), 1024-1032.
Ghafoor, S., Burger, I. A., & Vargas, A. H. (2019). Multimodality imaging of prostate cancer. Journal of Nuclear Medicine, 60(10), 1350-1358.
Gradishar, W. J., Moran, M. S., Abraham, J., Aft, R., Agnese, D., Allison, K. H., Anderson, B., Burstein, H. J., Chew, H., Dang, C., Elias, A. D., Giordano, S. H., Goetz, M. P., Goldstein, L. J., Hurvitz, S. A., Isakoff, S. J., Jankowitz, R. C., Javid, S. H., Krishnamurthy, J., . . . Kumar, R. (2022). Breast Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 20(6), 691-722. https://doi.org/10.6004/jnccn.2022.0030
Grochans, S., Cybulska, A. M., Simińska, D., Korbecki, J., Kojder, K., Chlubek, D., & Baranowska-Bosiacka, I. (2022). Epidemiology of glioblastoma multiforme–literature review. Cancers, 14(10), 2412.
Guo, R., Lu, G., Qin, B., & Fei, B. (2018). Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. Ultrasound Med Biol, 44(1), 37-70. https://doi.org/10.1016/j.ultrasmedbio.2017.09.012
Gupta, A., & Ritu, G. (2020). SN-am Dataset: White Blood Cancer Dataset of B-All and Mm for Stain Normalization. In: Accessed: Feb.
Halawani, A., Teynor, A., Setia, L., Brunner, G., & Burkhardt, H. (2006). Fundamentals and Applications of Image Retrieval: An Overview. Datenbank-Spektrum, 18(14-23), 6.
Han, K., Wang, Y., Chen, H., Chen, X., Guo, J., Liu, Z., Tang, Y., Xiao, A., Xu, C., & Xu, Y. (2022). A survey on vision transformer. IEEE transactions on pattern analysis and machine intelligence, 45(1), 87-110.
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,
Hernandez, V., Hansen, C. R., Widesott, L., Bäck, A., Canters, R., Fusella, M., Götstedt, J., Jurado-Bruggeman, D., Mukumoto, N., & Kaplan, L. P. (2020). What is plan quality in radiotherapy? The importance of evaluating dose metrics, complexity, and robustness of treatment plans. Radiotherapy and Oncology, 153, 26-33.
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.
Hooper, G. W., Ansari, S., Johnson, J. M., & Ginat, D. T. (2023). Advances in the Radiological Evaluation of and Theranostics for Glioblastoma. Cancers, 15(16), 4162.
Hu, X., Li, K., Han, J., Hua, X., Guo, L., & Liu, T. (2011). Bridging the semantic gap via functional brain imaging. IEEE Transactions on Multimedia, 14(2), 314-325.
Hu, Y., Yu, Z., Cheng, X., Luo, Y., & Wen, C. (2020). A bibliometric analysis and visualization of medical data mining research. Medicine, 99(22).
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,
Hussain, S., Mubeen, I., Ullah, N., Shah, S. S. U. D., Khan, B. A., Zahoor, M., Ullah, R., Khan, F. A., & Sultan, M. A. (2022). Modern diagnostic imaging technique applications and risk factors in the medical field: A review. BioMed Research International, 2022.
Iannessi, A., Beaumont, H., Liu, Y., & Bertrand, A.-S. (2021). RECIST 1.1 and lesion selection: How to deal with ambiguity at baseline? Insights into Imaging, 12(1), 1-9.
Jain, A. K., & Vailaya, A. (1996). Image retrieval using color and shape. Pattern recognition, 29(8), 1233-1244.
Kameda, T., Mizuma, Y., Taniguchi, H., Fujita, M., & Taniguchi, N. (2021). Point-of-care lung ultrasound for the assessment of pneumonia: a narrative review in the COVID-19 era. Journal of Medical Ultrasonics, 48, 31-43.
Karur, G. R., Aneja, A., Stojanovska, J., Hanneman, K., Latchamsetty, R., Kersting, D., & Rajiah, P. S. (2023). Imaging of Cardiac Fibrosis: An Update, From the AJR Special Series on Imaging of Fibrosis. AJR Am J Roentgenol. https://doi.org/10.2214/ajr.23.29870
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.
Kim, T.-H., Kim, I.-H., Kang, S. J., Choi, M., Kim, B.-H., Eom, B. W., Kim, B. J., Min, B.-H., Choi, C. I., & Shin, C. M. (2023). Korean Practice Guidelines for Gastric Cancer 2022: an evidence-based, multidisciplinary approach. Journal of Gastric Cancer, 23(1), 3.
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.
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.
Kouretas, I., & Paliouras, V. (2020). Hardware implementation of a softmax-like function for deep learning. Technologies, 8(3), 46.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Kumar, S. K., Callander, N. S., Adekola, K., Anderson, L., Baljevic, M., Campagnaro, E., Castillo, J. J., Chandler, J. C., Costello, C., Efebera, Y., Faiman, M., Garfall, A., Godby, K., Hillengass, J., Holmberg, L., Htut, M., Huff, C. A., Kang, Y., Hultcrantz, M., . . . Kumar, R. (2020). Multiple Myeloma, Version 3.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 18(12), 1685-1717. https://doi.org/10.6004/jnccn.2020.0057
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., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Levison, A. L., Baynes, K. M., Lowder, C. Y., Kaiser, P. K., & Srivastava, S. K. (2017). Choroidal neovascularisation on optical coherence tomography angiography in punctate inner choroidopathy and multifocal choroiditis. Br J Ophthalmol, 101(5), 616-622. https://doi.org/10.1136/bjophthalmol-2016-308806
Li, D., Dai, D., Chen, J., Xia, S., & Wang, G. (2023). Ensemble learning framework for image retrieval via deep hash ranking. Knowledge-Based Systems, 260, 110128.
Li, H., Rajbahadur, G. K., Lin, D., Bezemer, C.-P., & Jiang, Z. M. (2024). Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting. IEEE Access.
Li, L., Na, R., Mi, T., Cheng, H., Ma, L., & Chen, G. (2021). Medical Image Diagnostic Value of Computed Tomography for Bladder Tumors. Computational and Mathematical Methods in Medicine, 2021.
Li, M. D., Chang, K., Bearce, B., Chang, C. Y., Huang, A. J., Campbell, J. P., Brown, J. M., Singh, P., Hoebel, K. V., & Erdoğmuş, D. (2020). Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging. NPJ digital medicine, 3(1), 48.
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12), 6999-7019.
Liu, B., Lu, D., Wei, D., Wu, X., Wang, Y., Zhang, Y., & Zheng, Y. (2023). Improving medical vision-language contrastive pretraining with semantics-aware triage. IEEE Transactions on Medical Imaging.
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.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE/CVF international conference on computer vision,
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., & Xie, S. (2022). A convnet for the 2020s. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition,
Ljungberg, B., Albiges, L., Abu-Ghanem, Y., Bedke, J., Capitanio, U., Dabestani, S., Fernández-Pello, S., Giles, R. H., Hofmann, F., Hora, M., Klatte, T., Kuusk, T., Lam, T. B., Marconi, L., Powles, T., Tahbaz, R., Volpe, A., & Bex, A. (2022). European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol, 82(4), 399-410. https://doi.org/10.1016/j.eururo.2022.03.006
Lucchesi, F., & Aredes, N. Radiology Data from The Cancer Genome Atlas Stomach Adenocarcinoma [TCGA-STAD] collection, 2016. The Cancer Imaging Archive, 10, K9.
Lundy, P., Domino, J., Ryken, T., Fouke, S., McCracken, D. J., Ormond, D. R., & Olson, J. J. (2020). The role of imaging for the management of newly diagnosed glioblastoma in adults: a systematic review and evidence-based clinical practice guideline update. J Neurooncol, 150(2), 95-120. https://doi.org/10.1007/s11060-020-03597-3
Mackenzie, A., Lewis, E., & Loveland, J. (2023). Successes and challenges in extracting information from DICOM image databases for audit and research. Br J Radiol, 96(1151), 20230104. https://doi.org/10.1259/bjr.20230104
Malkov, Y. A., & Yashunin, D. A. (2018). Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. IEEE transactions on pattern analysis and machine intelligence, 42(4), 824-836.
Mansur, A. T., & Yildiz, S. (2019). A diagnostic challenge: inflamed and pigmented seborrheic keratosis. Clinical, dermoscopic, and histopathological correlation. Dermatol Online J, 25(3).
Masokano, I. B., Liu, W., Xie, S., Marcellin, D. F. H., Pei, Y., & Li, W. (2020). The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges. Cancer Imaging, 20(1), 1-11.
Maturen, K. E., Shampain, K. L., Roseland, M. E., Sakala, M. D., Zhang, M., & Stein, E. B. (2023). Malignant Epithelial Tumors of the Ovary: Pathogenesis and Imaging. Radiol Clin North Am, 61(4), 563-577. https://doi.org/10.1016/j.rcl.2023.02.003
Meng, S., Zhang, Y., Wang, H., Wang, L., Kong, T., Zhang, H., & Meng, S. (2021). Recent advances on TMDCs for medical diagnosis. Biomaterials, 269, 120471.
Motzer, R. J., Jonasch, E., Agarwal, N., Alva, A., Baine, M., Beckermann, K., Carlo, M. I., Choueiri, T. K., Costello, B. A., Derweesh, I. H., Desai, A., Ged, Y., George, S., Gore, J. L., Haas, N., Hancock, S. L., Kapur, P., Kyriakopoulos, C., Lam, E. T., . . . Motter, A. (2022). Kidney Cancer, Version 3.2022, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 20(1), 71-90. https://doi.org/10.6004/jnccn.2022.0001
Mun, S. K., Wong, K. H., Lo, S.-C. B., Li, Y., & Bayarsaikhan, S. (2021). Artificial intelligence for the future radiology diagnostic service. Frontiers in molecular biosciences, 7, 614258.
Murphy, S. J., & Werring, D. J. (2020). Stroke: causes and clinical features. Medicine, 48(9), 561-566.
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.
Neal, C. H., & Helvie, M. A. (2021). Overdiagnosis and risks of breast cancer screening. Radiologic Clinics, 59(1), 19-27.
Newitt, D., & Hylton, N. (2016). Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy. The Cancer Imaging Archive, 2.
Oeffinger, K. C., Fontham, E. T., Etzioni, R., Herzig, A., Michaelson, J. S., Shih, Y. C., Walter, L. C., Church, T. R., Flowers, C. R., LaMonte, S. J., Wolf, A. M., DeSantis, C., Lortet-Tieulent, J., Andrews, K., Manassaram-Baptiste, D., Saslow, D., Smith, R. A., Brawley, O. W., & Wender, R. (2015). Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. Jama, 314(15), 1599-1614. https://doi.org/10.1001/jama.2015.12783
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.
Öztürk, Ş., Çelik, E., & Çukur, T. (2023). Content-based medical image retrieval with opponent class adaptive margin loss. Information Sciences, 637, 118938.
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., & Kurc, T. (2020). AI in medical imaging informatics: current challenges and future directions. IEEE journal of biomedical and health informatics, 24(7), 1837-1857.
Paydary, K., Seraj, S. M., Zadeh, M. Z., Emamzadehfard, S., Shamchi, S. P., Gholami, S., Werner, T. J., & Alavi, A. (2019). The Evolving Role of FDG-PET/CT in the Diagnosis, Staging, and Treatment of Breast Cancer. Mol Imaging Biol, 21(1), 1-10. https://doi.org/10.1007/s11307-018-1181-3
Piras, L., & Giacinto, G. (2017). Information fusion in content based image retrieval: A comprehensive overview. Information Fusion, 37, 50-60.
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,
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., & Clark, J. (2021). Learning transferable visual models from natural language supervision. International conference on machine learning,
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.
Reinert, C. P., Baumgartner, K., Hepp, T., Bitzer, M., & Horger, M. (2020). Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase. Abdominal Radiology, 45, 750-758.
Ren, H. (2024). Precise treatment of pancreatic ductal adenocarcinoma. Cancer Letters, 216636.
Ruchalski, K., Braschi-Amirfarzan, M., Douek, M., Sai, V., Gutierrez, A., Dewan, R., & Goldin, J. (2021). A primer on RECIST 1.1 for oncologic imaging in clinical drug trials. Radiology: Imaging Cancer, 3(3), e210008.
Ruchalski, K., Dewan, R., Sai, V., McIntosh, L. J., & Braschi-Amirfarzan, M. (2022). Imaging response assessment for oncology: An algorithmic approach. European Journal of Radiology Open, 9, 100426.
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, 211-252.
Schaffter, T., Buist, D. S., Lee, C. I., Nikulin, Y., Ribli, D., Guan, Y., Lotter, W., Jie, Z., Du, H., & Wang, S. (2020). Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA network open, 3(3), e200265-e200265.
Selim, S., Kunkel, E., Wegier, P., Tanuseputro, P., Downar, J., Isenberg, S. R., Li, A., Kyeremanteng, K., Manuel, D., & Kobewka, D. M. (2020). A systematic review of interventions aiming to improve communication of prognosis to adult patients. Patient Education and Counseling, 103(8), 1467-1497.
Sharma, A., Kumar, R., & Garg, P. (2023). Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images. International Journal of Medical Informatics, 177, 105142.
Sheikhtaheri, A., Hasani, N., & Hosseini, A. (2023). Effect of picture archiving and communication system on diagnosis accuracy in CT and radiography examinations in emergency departments. International Journal of Medical Informatics, 170, 104972.
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual review of biomedical engineering, 19, 221-248.
Sherer, M. V., Lin, D., Elguindi, S., Duke, S., Tan, L.-T., Cacicedo, J., Dahele, M., & Gillespie, E. F. (2021). Metrics to evaluate the performance of auto-segmentation for radiation treatment planning: A critical review. Radiotherapy and Oncology, 160, 185-191.
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Singh, S. K., Sinha, A., Singh, H., Mahanti, A., Patel, A., Mahajan, S., Pandit, A. K., & Varadarajan, V. (2023). A novel deep learning-based technique for detecting prostate cancer in MRI images. Multimedia Tools and Applications, 1-15.
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: An overview of heart disease prediction. International Journal of computer applications, 17(8), 43-48.
Su, S.-S., Li, L.-Y., Wang, Y., & Li, Y.-Z. (2023). Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16. Frontiers in Neurology, 14, 1111906.
Sun, P., Simcha, D., Dopson, D., Guo, R., & Kumar, S. (2024). SOAR: improved indexing for approximate nearest neighbor search. Advances in Neural Information Processing Systems, 36.
Trepanier, C., Huang, A., Liu, M., & Ha, R. (2023). Emerging uses of artificial intelligence in breast and axillary ultrasound. Clin Imaging, 100, 64-68. https://doi.org/10.1016/j.clinimag.2023.05.007
Udayakumar, D., Madhuranthakam, A. J., & Doğan, B. E. (2024). Magnetic Resonance Perfusion Imaging for Breast Cancer. Magn Reson Imaging Clin N Am, 32(1), 135-150. https://doi.org/10.1016/j.mric.2023.09.012
Ursprung, S., Beer, L., Bruining, A., Woitek, R., Stewart, G. D., Gallagher, F. A., & Sala, E. (2020). Radiomics of computed tomography and magnetic resonance imaging in renal cell carcinoma—a systematic review and meta-analysis. European radiology, 30, 3558-3566.
Verma, M., & Raman, B. (2016). Local tri-directional patterns: A new texture feature descriptor for image retrieval. Digital Signal Processing, 51, 62-72.
Vishraj, R., Gupta, S., & Singh, S. (2022). A comprehensive review of content-based image retrieval systems using deep learning and hand-crafted features in medical imaging: Research challenges and future directions. Computers and Electrical Engineering, 104, 108450.
Vogel, J. D., Felder, S. I., Bhama, A. R., Hawkins, A. T., Langenfeld, S. J., Shaffer, V. O., Thorsen, A. J., Weiser, M. R., Chang, G. J., & Lightner, A. L. (2022). The American Society of Colon and Rectal Surgeons clinical practice guidelines for the management of colon cancer. Diseases of the Colon & Rectum, 65(2), 148-177.
Walker, S. M., Lim, I., Lindenberg, L., Mena, E., Choyke, P. L., & Turkbey, B. (2020). Positron emission tomography (PET) radiotracers for prostate cancer imaging. Abdominal Radiology, 45, 2165-2175.
Wang, J. Z., Li, J., & Wiederhold, G. (2001). SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE transactions on pattern analysis and machine intelligence, 23(9), 947-963.
Wang, M., Zhu, W., Shi, F., Su, J., Chen, H., Yu, K., Zhou, Y., Peng, Y., Chen, Z., & Chen, X. (2021). MsTGANet: Automatic drusen segmentation from retinal OCT images. IEEE Transactions on Medical Imaging, 41(2), 394-406.
Wang, S., Han, K., & Jin, J. (2019). Review of image low-level feature extraction methods for content-based image retrieval. Sensor Review, 39(6), 783-809.
Wang, Z., Wu, Z., Agarwal, D., & Sun, J. (2022). Medclip: Contrastive learning from unpaired medical images and text. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing,
Wilkinson, N. (2020). Management of rectal cancer. Surgical Clinics, 100(3), 615-628.
Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. (2018). Cbam: Convolutional block attention module. Proceedings of the European conference on computer vision (ECCV),
Yang, Y., Jiao, S., He, J., Xia, B., Li, J., & Xiao, R. (2020). Image retrieval via learning content-based deep quality model towards big data. Future Generation Computer Systems, 112, 243-249.
Yasmin, M., Sharif, M., & Mohsin, S. (2013). Use of low level features for content based image retrieval: Survey. Res J Recent Sci, ISSN, 2277, 2502.
Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J.-F., & Hua, L. (2012). Data Mining in Healthcare and Biomedicine: A Survey of the Literature. Journal of Medical Systems, 36(4), 2431-2448. https://doi.org/10.1007/s10916-011-9710-5
Zenggang, X., Zhiwen, T., Xiaowen, C., Xue-min, Z., Kaibin, Z., & Conghuan, Y. (2021). Research on image retrieval algorithm based on combination of color and shape features. Journal of signal processing systems, 93, 139-146.
Zhang, G., Wu, Z., Zhang, X., Xu, L., Mao, L., Li, X., Xiao, Y., Ji, Z., Sun, H., & Jin, Z. (2022). CT-based radiomics to predict muscle invasion in bladder cancer. Eur Radiol, 32(5), 3260-3268. https://doi.org/10.1007/s00330-021-08426-3
Zhang, Y., Xie, F., Song, X., Zheng, Y., Liu, J., & Wang, J. (2022). Dermoscopic image retrieval based on rotation-invariance deep hashing. Medical Image Analysis, 77, 102301.
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.
衛生福利部. (2022). 111年國人死因統計結果. https://www.mohw.gov.tw/cp-16-74869-1.html |
| Description: | 碩士
國立政治大學
圖書資訊與檔案學研究所
111155020 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0111155020 |
| Data Type: | thesis |
| Appears in Collections: | [圖書資訊與檔案學研究所] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 502001.pdf | | 7040Kb | Adobe PDF | 0 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
