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Research Article

Hierarchical active learning for defect localization in 3D systems

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  • Agathos, K., Chatzi, E., & Bordas, S. (2018). Multiple crack detection in 3d using a stable xfem and global optimization. Computational Mechanics, 62(4), 835–852. https://doi.org/10.1007/s00466-017-1532-y
  • Aliev, R. R., & Panfilov, A. V. (1996a). Modeling of heart excitation patterns caused by a local inhomogeneity. Journal of Theoretical Biology, 181(1), 33–40. https://doi.org/10.1006/jtbi.1996.0112
  • Aliev, R. R., & Panfilov, A. V. (1996b). A simple two-variable model of cardiac excitation. Chaos, Solitons & Fractals, 7(3), 293–301. https://doi.org/10.1016/0960-0779(95)00089-5
  • Arif, M., Malagore, I. A., & Afsar, F. A. (2012). Detection and localization of myocardial infarction using k-nearest neighbor classifier. Journal of Medical Systems, 36(1), 279–289. https://doi.org/10.1007/s10916-010-9474-3
  • Astudillo, R., & Frazier, P. (2019). Bayesian optimization of composite functions. In International Conference on Machine Learning (pp. 354–363). PMLR.
  • Balasubramanian, M., & Schwartz, E. L. (2002). The isomap algorithm and topological stability. Science, 295(5552), 7–7. https://doi.org/10.1126/science.295.5552.7a
  • Barr, R. C., Ramsey, M., & Spach, M. S. (1977). Relating epicardial to body surface potential distributions by means of transfer coefficients based on geometry measurements. IEEE Transactions on Bio-Medical Engineering, 24(1), 1–11. https://doi.org/10.1109/TBME.1977.326201
  • Bonilla, E. V., Chai, K., & Williams, C. (2007). Multi-task gaussian process prediction. Advances in Neural Information Processing Systems, 20, 1–8.
  • Bousselham, A., Bouattane, O., Youssfi, M., & Raihani, A. (2018). 3d brain tumor localization and parameter estimation using thermographic approach on gpu. Journal of Thermal Biology, 71, 52–61. https://doi.org/10.1016/j.jtherbio.2017.10.014
  • Brochu, E., Cora, V. M., & De Freitas, N. (2010). A tutorial on bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599.
  • Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190–1208. https://doi.org/10.1137/0916069
  • Chan, T. M. (1996). Optimal output-sensitive convex hull algorithms in two and three dimensions. Discrete & Computational Geometry, 16(4), 361–368. https://doi.org/10.1007/BF02712873
  • Chen, H., Zheng, L., Al Kontar, R., & Raskutti, G. (2020). Stochastic gradient descent in correlated settings: A study on gaussian processes. Advances in Neural Information Processing Systems, 33, 2722–2733.
  • Chen, S., Wang, Z., Yao, B., & Liu, T. (2022). Prediction of diabetic retinopathy using longitudinal electronic health records. In 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (pp. 949–954). IEEE. https://doi.org/10.1109/CASE49997.2022.9926605
  • Chinchapatnam, P., Rhode, K. S., Ginks, M., Rinaldi, C. A., Lambiase, P., Razavi, R., Arridge, S., & Sermesant, M. (2008). Model-based imaging of cardiac apparent conductivity and local conduction velocity for diagnosis and planning of therapy. IEEE Transactions on Medical Imaging, 27(11), 1631–1642. https://doi.org/10.1109/TMI.2008.2004644
  • Datta, A., Banerjee, S., Finley, A. O., & Gelfand, A. E. (2016). Hierarchical nearest-neighbor gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111(514), 800–812. https://doi.org/10.1080/01621459.2015.1044091
  • Dawoud, F., Wagner, G. S., Moody, G., & Horáček, B. M. (2008). Using inverse electrocardiography to image myocardial infarction – Reflecting on the 2007 physionet/computers in cardiology challenge. Journal of Electrocardiology, 41(6), 630–635. https://doi.org/10.1016/j.jelectrocard.2008.07.022
  • Deng, Z., Tutunnikov, I., Averbukh, I. S., Thachuk, M., & Krems, R. (2020). Bayesian optimization for inverse problems in time-dependent quantum dynamics. The Journal of Chemical Physics, 153(16), 164111. https://doi.org/10.1063/5.0015896
  • Dhamala, J., Arevalo, H. J., Sapp, J., Horacek, M., Wu, K. C., Trayanova, N. A., & Wang, L. (2017). Spatially adaptive multi-scale optimization for local parameter estimation in cardiac electrophysiology. IEEE Transactions on Medical Imaging, 36(9), 1966–1978. https://doi.org/10.1109/TMI.2017.2697820
  • Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1(1), 269–271. https://doi.org/10.1007/BF01386390
  • Giffard-Roisin, S., Jackson, T., Fovargue, L., Lee, J., Delingette, H., Razavi, R., Ayache, N., & Sermesant, M. (2017). Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping. IEEE Transactions on Bio-Medical Engineering, 64(9), 2206–2218. https://doi.org/10.1109/TBME.2016.2629849
  • 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.-K., & Stanley, H. E. (2000). Physiobank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation, 101(23), e215–e220. https://doi.org/10.1161/01.cir.101.23.e215
  • Graham, R. L. (1972). An efficient algorithm for determining the convex hull of a finite planar set. Information Processing Letters, 1(4), 132–133. https://doi.org/10.1016/0020-0190(72)90045-2
  • Huang, C., Ren, Y., McGuinness, E. K., Losego, M. D., Lively, R. P., & Joseph, V. R. (2021). Bayesian optimization of functional output in inverse problems. Optimization and Engineering, 22(4), 2553–2574. https://doi.org/10.1007/s11081-021-09677-1
  • Johannesson, G., Glaser, R., Lee, C., Nitao, J., & Hanley, W. (2005). Multi-resolution markov-chain-monte-carlo approach for system identification with an application to finite-element models. Technical report, Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States).
  • Kanagawa, M., Hennig, P., Sejdinovic, D., & Sriperumbudur, B. K. (2018). Gaussian processes and kernel methods: A review on connections and equivalences. arXiv preprint arXiv:1807.02582.
  • Kang, S., Jin, R., Deng, X., & Kenett, R. S. (2023). Challenges of modeling and analysis in cybermanufacturing: A review from a machine learning and computation perspective. Journal of Intelligent Manufacturing, 34(2), 415–428. https://doi.org/10.1007/s10845-021-01817-9
  • Koutsourelakis, P.-S. (2009). A multi-resolution, non-parametric, bayesian framework for identification of spatially-varying model parameters. Journal of Computational Physics, 228(17), 6184–6211. https://doi.org/10.1016/j.jcp.2009.05.016
  • Lahoti, G., Chen, J., Yue, X., Yan, H., Ranjan, C., Qian, Z., Zhang, C., & Wang, B. (2021). Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images. IISE Transactions on Healthcare Systems Engineering, 11(4), 1–17. https://doi.org/10.1080/24725579.2021.1910599
  • Latz, J., Papaioannou, I., & Ullmann, E. (2018). Multilevel sequential2 monte carlo for bayesian inverse problems. Journal of Computational Physics, 368, 154–178. https://doi.org/10.1016/j.jcp.2018.04.014
  • Lawrence, N. D., & Moore, A. J. (2007). Hierarchical gaussian process latent variable models. In Proceedings of the 24th international conference on machine learning (pp. 481–488). https://doi.org/10.1145/1273496.1273557
  • Lee, C., Wang, K., Wu, J., Cai, W., & Yue, X. (2021). Partitioned active learning for heterogeneous systems. arXiv preprint arXiv:2105.08547.
  • Li, X., Jia, X., Yang, Q., & Lee, J. (2020). Quality analysis in metal additive manufacturing with deep learning. Journal of Intelligent Manufacturing, 31(8), 2003–2017. https://doi.org/10.1007/s10845-020-01549-2
  • Lian, C., Liu, M., Zhang, J., & Shen, D. (2020). Hierarchical fully convolutional network for joint atrophy localization and alzheimer’s disease diagnosis using structural mri. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(4), 880–893. https://doi.org/10.1109/TPAMI.2018.2889096
  • Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S. (2019). Automated defect inspection of led chip using deep convolutional neural network. Journal of Intelligent Manufacturing, 30(6), 2525–2534. https://doi.org/10.1007/s10845-018-1415-x
  • Moreau-Villéger, V., Delingette, H., Sermesant, M., Ashikaga, H., McVeigh, E., & Ayache, N. (2006). Building maps of local apparent conductivity of the epicardium with a 2-d electrophysiological model of the heart. IEEE Transactions on Bio-Medical Engineering, 53(8), 1457–1466. https://doi.org/10.1109/TBME.2006.877794
  • Moriconi, R., Kumar, K. S., & Deisenroth, M. P. (2020). High-dimensional bayesian optimization with projections using quantile gaussian processes. Optimization Letters, 14(1), 51–64. https://doi.org/10.1007/s11590-019-01433-w
  • Olson, L. G., & Throne, R. D. (2010). Numerical simulation of an inverse method for tumour size and location estimation. Inverse Problems in Science and Engineering, 18(6), 813–834. https://doi.org/10.1080/17415977.2010.497965
  • Park, S., & Choi, S. (2010). Hierarchical gaussian process regression. In Proceedings of 2nd Asian conference on machine learning (95–110). JMLR Workshop and Conference Proceedings.
  • Rao, C., Sun, H., & Liu, Y. (2020). Physics-informed deep learning for incompressible laminar flows. Theoretical and Applied Mechanics Letters, 10(3), 207–212. https://doi.org/10.1016/j.taml.2020.01.039
  • Rao, C., Sun, H., & Liu, Y. (2021). Physics-informed deep learning for computational elastodynamics without labeled data. Journal of Engineering Mechanics, 147(8), 04021043. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001947
  • Roy, S., Menapace, W., Oei, S., Luijten, B., Fini, E., Saltori, C., Huijben, I., Chennakeshava, N., Mento, F., Sentelli, A., Peschiera, E., Trevisan, R., Maschietto, G., Torri, E., Inchingolo, R., Smargiassi, A., Soldati, G., Rota, P., Passerini, A., … Demi, L. (2020). Deep learning for classification and localization of covid-19 markers in point-of-care lung ultrasound. IEEE Transactions on Medical Imaging, 39(8), 2676–2687. https://doi.org/10.1109/TMI.2020.2994459
  • Sharma, L., Tripathy, R., & Dandapat, S. (2015). Multiscale energy and eigenspace approach to detection and localization of myocardial infarction. IEEE Transactions on Bio-Medical Engineering, 62(7), 1827–1837. https://doi.org/10.1109/TBME.2015.2405134
  • Shen, B., Gnanasambandam, R., Wang, R., & Kong, Z. J. (2022). Multi-task gaussian process upper confidence bound for hyperparameter tuning and its application for simulation studies of additive manufacturing. IISE Transactions, 55(5), 1–13.
  • Shi, J. Q., Murray-Smith, R., & Titterington, D. M. (2005). Hierarchical gaussian process mixtures for regression. Statistics and Computing, 15(1), 31–41. https://doi.org/10.1007/s11222-005-4787-7
  • Srinivas, N., Krause, A., Kakade, S. M., & Seeger, M. (2009). Gaussian process optimization in the bandit setting: No regret and experimental design. arXiv preprint arXiv:0912.3995.
  • Swersky, K., Snoek, J., & Adams, R. P. (2013). Multi-task bayesian optimization. Advances in Neural Information Processing Systems, 26, 1–9.
  • Thompson, S. M., Bian, L., Shamsaei, N., & Yadollahi, A. (2015). An overview of direct laser deposition for additive manufacturing; part i: Transport phenomena, modeling and diagnostics. Additive Manufacturing, 8, 36–62. https://doi.org/10.1016/j.addma.2015.07.001
  • Tripathy, R. K., Bhattacharyya, A., & Pachori, R. B. (2019). Localization of myocardial infarction from multi-lead ecg signals using multiscale analysis and convolutional neural network. IEEE Sensors Journal, 19(23), 11437–11448. https://doi.org/10.1109/JSEN.2019.2935552
  • Ulmasov, D., Baroukh, C., Chachuat, B., Deisenroth, M. P., & Misener, R. (2016). Bayesian optimization with dimension scheduling: Application to biological systems. In Computer aided chemical engineering (vol. 38, pp. 1051–1056). Elsevier.
  • Van Wyk, F., Khojandi, A., & Kamaleswaran, R. (2019). Improving prediction performance using hierarchical analysis of real-time data: A sepsis case study. IEEE Journal of Biomedical and Health Informatics, 23(3), 978–986. https://doi.org/10.1109/JBHI.2019.2894570
  • Wan, J., & Zabaras, N. (2011). A Bayesian approach to multiscale inverse problems using the sequential monte carlo method. Inverse Problems, 27(10), 105004. https://doi.org/10.1088/0266-5611/27/10/105004
  • Wang, K., Pleiss, G., Gardner, J., Tyree, S., Weinberger, K. Q., & Wilson, A. G. (2019). Exact gaussian processes on a million data points. Advances in Neural Information Processing Systems, 32, 1–12.
  • Wang, L., Hawkins-Daarud, A., Swanson, K. R., Hu, L. S., & Li, J. (2022). Knowledge-infused global-local data fusion for spatial predictive modeling in precision medicine. IEEE Transactions on Automation Science and Engineering, 19(3), 2203–2215. https://doi.org/10.1109/TASE.2021.3076117
  • Wang, L., Wong, K. C., Zhang, H., Liu, H., & Shi, P. (2011). Noninvasive computational imaging of cardiac electrophysiology for 3-d infarct. IEEE Transactions on Bio-Medical Engineering, 58(4), 1033–1043. https://doi.org/10.1109/TBME.2010.2099226
  • Wang, Z., Stavrakis, S., & Yao, B. (2023). Hierarchical deep learning with generative adversarial network for automatic cardiac diagnosis from Ecg signals. Computers in Biology and Medicine, 155, 106641. https://doi.org/10.1016/j.compbiomed.2023.106641
  • Wang, Z., & Yao, B. (2022). Multi-branching temporal convolutional network for sepsis prediction. IEEE Journal of Biomedical and Health Informatics, 26(2), 876–887. https://doi.org/10.1109/JBHI.2021.3092835
  • Wang, Z., Zoghi, M., Hutter, F., Matheson, D., De Freitas, N. (2013). Bayesian optimization in high dimensions via random embeddings. IJCAI, 13, 1778–1784.
  • Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine learning (vol. 2). MIT press.
  • Winter, J., Abaidi, R., Kaiser, J., Adami, S., & Adams, N. (2023). Multi-fidelity bayesian optimization to solve the inverse Stefan problem. Computer Methods in Applied Mechanics and Engineering, 410, 115946. https://doi.org/10.1016/j.cma.2023.115946
  • Xie, J., & Yao, B. (2022a). Physics-constrained deep active learning for spatiotemporal modeling of cardiac electrodynamics. Computers in Biology and Medicine, 146, 105586. https://doi.org/10.1016/j.compbiomed.2022.105586
  • Xie, J., & Yao, B. (2022b). Physics-constrained deep learning for robust inverse Ecg modeling. IEEE Transactions on Automation Science and Engineering, 20(1), 151–166. https://doi.org/10.1109/TASE.2022.3144347
  • Yan, C., Yao, J., Li, R., Xu, Z., & Huang, J. (2018). Weakly supervised deep learning for thoracic disease classification and localization on chest x-rays. In Proceedings of the 2018 ACM international conference on bioinformatics, computational biology, and health informatics (pp. 103–110). https://doi.org/10.1145/3233547.3233573
  • Yao, B. (2021). Spatiotemporal modeling and optimization for personalized cardiac simulation. IISE Transactions on Healthcare Systems Engineering, 11(2), 145–160. https://doi.org/10.1080/24725579.2021.1879322
  • Yao, B., Imani, F., Sakpal, A. S., Reutzel, E., & Yang, H. (2018). Multifractal analysis of image profiles for the characterization and detection of defects in additive manufacturing. Journal of Manufacturing Science and Engineering, 140(3), 4037891. https://doi.org/10.1115/1.4037891
  • Yao, B., & Yang, H. (2016). Physics-driven spatiotemporal regularization for high-dimensional predictive modeling: A novel approach to solve the inverse ecg problem. Scientific Reports, 6(1), 1–13. https://doi.org/10.1038/srep39012
  • Yao, B., & Yang, H. (2021). Spatiotemporal regularization for inverse ecg modeling. IISE Transactions on Healthcare Systems Engineering, 11(1), 11–23. https://doi.org/10.1080/24725579.2020.1823531
  • Yin, X., Chen, Y., Bouferguene, A., Zaman, H., Al-Hussein, M., & Kurach, L. (2020). A deep learning-based framework for an automated defect detection system for sewer pipes. Automation in Construction, 109, 102967. https://doi.org/10.1016/j.autcon.2019.102967
  • Zhao, X., Yan, H., Hu, Z., & Du, D. (2022). Deep spatio-temporal sparse decomposition for trend prediction and anomaly detection in cardiac electrical conduction. IISE Transactions on Healthcare Systems Engineering, 12(2), 150–164. https://doi.org/10.1080/24725579.2021.1982081

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