Một phương pháp đo lường sự tương đồng mới dựa trên sự tương ứng giữa các đặc trưng Bayesian Network cho việc truy xuất các trường hợp u não

M. Nilsson et al. (eds.), Advancements and Trends in Medical Case-Based Reasoning: An Overview of Systems and System Development, (2004). B. V. Ginneken et al., Computer-aided diagnosis in chest radiography: A survey, in IEEE Transactions on Medical Imaging (2001), pp. 1228–1241. B. J. Erickson and B. Bartholmai, Computer-aided detection and diagnosis at the start of the third millennium, J Digit Imaging, 15(2),(2002), 59–68. M. O. Güld et al., A generic concept for the implementation of medical image retrieval systems, Int J Med Info (2006). L. G. Shapiro et al., Similarity-Based Retrieval for Biomedical Applications. Case-Based Reasoning on Images and Signals Studies in Computational Intelligence, 73 (2008) 355–387. I. Bichindaritz and C. Marling, Case-based reasoning in the health sciences: What’s next?, Artif Intell Med. 36(2)(2006) 127–135. S. Montani, How to use contextual knowledge in medical case-based reasoning systems: a survey on very recent trends, Artif Intell Med. 51(2)(2011) 125–131 H. Greenspan and A. Pinhas, Medical Image Categorization and Retrieval for PACS Using the GMM-KL Framework, IEEE Transactions on Information Technology in Biomedicine, 11(2)(2007) 190–202. Y. Liu et al., Semantic-based biomedical image indexing and retrieval, in Proc. International Conference on Diagnostic Imaging and Analysis, (2002), pp. 18–20. S. Clinchant et al., Semantic combination of textual and visual information in multimedia retrieval, in Proc. 1st ACM International Conference on Multimedia Retrieval, (New York, USA, 2011), pp. 1–8. C. B. Akgül et al., Content-Based Image Retrieval in Radiology: Current Status and Future Directions, J Digit Imaging, 24 (2) (2011) 2011–208. C.-R. Shyu et al., ASSERT: A Physician-in-the-Loop Content-Based Retrieval System for HRCT Image Databases, Computer Vision and Image Understanding, 75 (1) (1999) 1999–111. T. M. Lehmann et al., The IRMA Project: A State of the Art Report, in Proc. Korea-Germany Workshop on Advanced Medical Image, (2003), pp. 161–171. D. Nikovski, Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics, IEEE Trans. on Knowl. and Data Eng., 12(4) (2000) 509–516. I. J. Cox et al., The Bayesian image retrieval system, PicHunter: theory, implementation, and psychophysical experiments, IEEE Transactions on Image Processing, 9 (1) (2000) 2000–20. J. Luo et al., A Bayesian network-based framework for semantic image understanding, Pattern Recognition Journal, 38 (6) (2005) 2005–919. G. Quellec et al., Multimodal medical case retrieval using Bayesian networks and the Dezert-Smarandache theory, in Proc. 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (2008), pp. 245–248. D. N. Louis et al., The 2007 WHO Classification of Tumours of the Central Nervous System, Acta Neuropathol Journal, 114 (2) (2007) 2007–97. Y-E. L. Koo et al., Brain cancer diagnosis and therapy with nanoplatforms, Adv. Drug Deliv. Rev., 58(14) (2006) 1556–1577. R. N. Al-Okaili et al., Advanced MR Imaging Techniques in the Diagnosis of Intraaxial Brain Tumors in Adults, Radiographics Journal, 26 (1) (2006) 2006–173. O. Pourret et al. (eds.), Bayesian Networks: A Practical Guide to Applications, (1st ed. Wiley, 2008). J. Pearl (eds.), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1st edn. (Morgan Kaufmann, 1988). N. Harris et al., Criticizing Conditional Probabilities in Belief Networks, in Proc. Annu Symp Comput Appl Med Care, (1990), pp. 805–809. J. Cheng et al., Learning Bayesian Networks from Data: An Information-Theory Based Approach, Artificial Intelligence, 137(1–2) (2002) 43–90. S. Jouili and S. Tabbone, Graph Matching Based on Node Signatures, in Proc. the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, (Berlin, Heidelberg, 2009), pp. 154–163. L. Torresani et al. “ Feature Correspondence Via Graph Matching: Models and Global Optimization,” in Proc. the 10th European Conference on Computer Vision, (Berlin, Heidelberg, 2008), pp. 596–609. R. Bergmann and A. Stahl, Similarity measures for object-oriented case representations, Advances in Case-Based Reasoning Lecture Notes in Computer Science, 1488 (1998) 25–36. H. Bunke and B. T. Messmer, Similarity measures for structured representations, in Topics in Case-Based Reasoning, (Eds. Springer Berlin Heidelberg, 1994), pp. 106–118. P. Cunningham, A Taxonomy of Similarity Mechanisms for Case-Based Reasoning, IEEE Transactions on Knowledge and Data Engineering, 21 (11) (2009) 2009–1532. F. Campillo et al., Computational probability modeling and Bayesian inference, ARIMA, 9 (2008) 123–143. K. P. Murphy et al., Loopy Belief Propagation for Approximate Inference: An Empirical Study, in Proc. of Uncertainty in AI, (1999), pp. 467–475. H. Yazid et al., MRI cases containing cerebral tumors retrieval using Bayesian networks, in Proc. the 10th IEEE International Symposium on Signal Processing and Information Technology, (Washington, DC, USA, 2010), pp. 7–12. P. Zezula et al. (eds.), Similarity Search - The Metric Space Approach, (1st Springer Publishing, 2010). L. Leydesdorff, Similarity Measures, Analysis, and Information Theory, CoRR, abs/0911.4292 (2009). R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in Proc. the 14th international joint conference on Artificial intelligence, (San Francisco, CA, USA, 1995), pp. 1137–1143. B. Efron, Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation, Journal of the American Statistical Association, 78 (382) (1983) 1983–316.