Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Generative AI-Driven Human Digital Twin in IoT-Healthcare: A Comprehensive Survey (2401.13699v2)

Published 22 Jan 2024 in cs.HC, cs.AI, and cs.LG

Abstract: The Internet of things (IoT) can significantly enhance the quality of human life, specifically in healthcare, attracting extensive attentions to IoT-healthcare services. Meanwhile, the human digital twin (HDT) is proposed as an innovative paradigm that can comprehensively characterize the replication of the individual human body in the digital world and reflect its physical status in real time. Naturally, HDT is envisioned to empower IoT-healthcare beyond the application of healthcare monitoring by acting as a versatile and vivid human digital testbed, simulating the outcomes and guiding the practical treatments. However, successfully establishing HDT requires high-fidelity virtual modeling and strong information interactions but possibly with scarce, biased and noisy data. Fortunately, a recent popular technology called generative artificial intelligence (GAI) may be a promising solution because it can leverage advanced AI algorithms to automatically create, manipulate, and modify valuable while diverse data. This survey particularly focuses on the implementation of GAI-driven HDT in IoT-healthcare. We start by introducing the background of IoT-healthcare and the potential of GAI-driven HDT. Then, we delve into the fundamental techniques and present the overall framework of GAI-driven HDT. After that, we explore the realization of GAI-driven HDT in detail, including GAI-enabled data acquisition, communication, data management, digital modeling, and data analysis. Besides, we discuss typical IoT-healthcare applications that can be revolutionized by GAI-driven HDT, namely personalized health monitoring and diagnosis, personalized prescription, and personalized rehabilitation. Finally, we conclude this survey by highlighting some future research directions.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (145)
  1. S. Selvaraj and S. Sundaravaradhan, “Challenges and opportunities in IoT healthcare systems: a systematic review,” SN Appl. Sci., vol. 2, no. 1, p. 139, 2020.
  2. M. H. Kashani, M. Madanipour, M. Nikravan et al., “A systematic review of IoT in healthcare: Applications, techniques, and trends,” J. Netw. Comput. Appl., vol. 192, p. 103164, 2021.
  3. G. H. Hub, “Global heart hub,” https://globalhearthub.org, 2023.
  4. H. Habibzadeh, K. Dinesh, O. R. Shishvan et al., “A survey of healthcare internet of things (HIoT): A clinical perspective,” IEEE Internet Things J., vol. 7, no. 1, pp. 53–71, 2020.
  5. Y. Yang, H. Wang, R. Jiang et al., “A review of IoT-enabled mobile healthcare: technologies, challenges, and future trends,” IEEE Internet Things J., vol. 9, no. 12, pp. 9478–9502, 2022.
  6. the Computational Science Lab (Alfons Hoekstra) of the Informatics Institute, “Ecosystem for digital twins in healthcare,” https://www.edith-csa.eu/, 2023.
  7. B. Wang, H. Zhou, X. Li et al., “Human digital twin in the context of Industry 5.0,” Robotics and Computer-Integrated Manufacturing, vol. 85, p. 102626, 2024.
  8. J. Chen, C. Yi, S. D. Okegbile et al., “Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey,” IEEE Commun. Surv. Tutor., pp. 1–1, 2023.
  9. S. D. Okegbile, J. Cai, C. Yi et al., “Human digital twin for personalized healthcare: Vision, architecture and future directions,” IEEE Netw., 2022.
  10. A. E. Coşgun, “Digital twin and human digital twin for practical implementation in industry 5.0,” in Global Perspectives on Robotics and Autonomous Systems: Development and Applications.   IGI Global, 2023, pp. 168–183.
  11. B. Björnsson, C. Borrebaeck, N. Elander et al., “Digital twins to personalize medicine,” Genome Med., vol. 12, pp. 1–4, 2020.
  12. Google, “Med-palm,” https://sites.research.google/med-palm/, 2023.
  13. J. Chen, C. Yi, H. Du et al., “A revolution of personalized healthcare: Enabling human digital twin with mobile AIGC,” arXiv preprint arXiv:2307.12115, 2023.
  14. H. Du, R. Zhang, Y. Liu, J. Wang, Y. Lin, Z. Li, D. Niyato, J. Kang, Z. Xiong, S. Cui et al., “Beyond deep reinforcement learning: A tutorial on generative diffusion models in network optimization,” arXiv preprint arXiv:2308.05384, 2023.
  15. S. Bond-Taylor, A. Leach, Y. Long et al., “Deep generative modelling: A comparative review of VAEs, GANs, normalizing flows, energy-based and autoregressive models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 11, pp. 7327–7347, 2022.
  16. Y. Lin, L. Chen, A. Ali et al., “Human digital twin: A survey,” arXiv preprint arXiv:2212.05937, 2022.
  17. M. Xu, H. Du, D. Niyato et al., “Unleashing the power of edge-cloud generative AI in mobile networks: A survey of aigc services,” arXiv preprint arXiv:2303.16129, 2023.
  18. B. Wang, H. Zhou, X. Li et al., “Human digital twin in the context of industry 5.0,” Robot. Comput.-Integr. Manuf., vol. 85, p. 102626, 2024.
  19. J. Chen, C. Yi, S. D. Okegbile et al., “Networking architecture and key supporting technologies for human digital twin in personalized healthcare: A comprehensive survey,” IEEE Commun. Surv. Tutor., 2023.
  20. P. Pataranutaporn, V. Danry, J. Leong et al., “AI-generated characters for supporting personalized learning and well-being,” Nat. Mach. Intell., vol. 3, no. 12, pp. 1013–1022, 2021.
  21. H. Pascual, X. M. Bruin, A. Alonso et al., “A systematic review on human modeling: Digging into human digital twin implementations,” arXiv preprint arXiv:2302.03593, 2023.
  22. T. Sun, X. He, and Z. Li, “Digital twin in healthcare: Recent updates and challenges,” Digit. Health, vol. 9, p. 20552076221149651, 2023.
  23. M. AlAmir and M. AlGhamdi, “The role of generative adversarial network in medical image analysis: An in-depth survey,” ACM Comput. Surv., vol. 55, no. 5, pp. 1–36, 2022.
  24. Y. Shokrollahi, S. Yarmohammadtoosky, M. M. Nikahd et al., “A comprehensive review of generative ai in healthcare,” arXiv preprint arXiv:2310.00795, 2023.
  25. E. Research, “Digital human avatar market, by product type (interactive digital human avatar and non-interactive digital human avatar), by industry verticals (gaming, retail, it & telecommunications, education, and others), and by region forecast to 2032,” Emergen Research, 2023. [Online]. Available: https://www.emergenresearch.com/industry-report/digital-human-avatar-market
  26. H. Lonsdale, G. M. Gray, L. M. Ahumada et al., “The perioperative human digital twin,” Anesth. Analg., vol. 134, no. 4, pp. 885–892, 2022.
  27. F. Tao, H. Zhang, A. Liu et al., “Digital twin in industry: State-of-the-art,” IEEE Trans. Industr. Inform., vol. 15, no. 4, pp. 2405–2415, 2019.
  28. S. Jang, J. Jeong, J. Lee et al., “Digital twin for intelligent network: Data lifecycle, digital replication, and ai-based optimizations,” IEEE Commun., pp. 1–7, 2023.
  29. Z. Hu, S. Lou, Y. Xing et al., “Review and perspectives on driver digital twin and its enabling technologies for intelligent vehicles,” IEEE Trans. Intell. Veh., vol. 7, no. 3, pp. 417–440, 2022.
  30. A. Bilberg and A. A. Malik, “Digital twin driven human–robot collaborative assembly,” CIRP annals, vol. 68, no. 1, pp. 499–502, 2019.
  31. B. M. Rosa and G. Z. Yang, “A flexible wearable device for measurement of cardiac, electrodermal, and motion parameters in mental healthcare applications,” IEEE J. Biomed. Health Inform., vol. 23, no. 6, pp. 2276–2285, 2019.
  32. R. Ferdousi, F. Laamarti, M. A. Hossain et al., “Digital twins for well-being: an overview,” Digital Twin, vol. 1, p. 7, 2022.
  33. H. Y. Zhu, N. Q. Hieu, D. T. Hoang et al., “A human-centric metaverse enabled by brain-computer interface: A survey,” arXiv preprint arXiv:2309.01848, 2023.
  34. J. Wu, W. Gan, Z. Chen et al., “AI-generated content (AIGC): A survey,” arXiv preprint arXiv:2304.06632, 2023.
  35. A. Creswell, T. White, V. Dumoulin et al., “Generative adversarial networks: An overview,” IEEE Signal Process. Mag, vol. 35, no. 1, pp. 53–65, 2018.
  36. T. Golany, K. Radinsky, and D. Freedman, “SimGANs: Simulator-based generative adversarial networks for ECG synthesis to improve deep ECG classification,” in Proc. ICML.   PMLR, 2020, pp. 3597–3606.
  37. F. Lau, T. Hendriks, J. Lieman-Sifry et al., “ScarGAN: chained generative adversarial networks to simulate pathological tissue on cardiovascular MR scans,” in Proc. DLMIA.   Springer, 2018, pp. 343–350.
  38. D. P. Kingma, M. Welling et al., “An introduction to variational autoencoders,” Foundations and Trends in Machine Learning, vol. 12, no. 4, pp. 307–392, 2019.
  39. A. Dittadi, S. Dziadzio, D. Cosker, and ohters, “Full-body motion from a single head-mounted device: Generating SMPL poses from partial observations,” in Proc. IEEE/CVF ICCV, 2021, pp. 11 687–11 697.
  40. A. Allen, A. Siefkas, E. Pellegrini et al., “A digital twins machine learning model for forecasting disease progression in stroke patients,” Appl. Sci., vol. 11, no. 12, p. 5576, 2021.
  41. A. Vaswani, N. Shazeer, N. Parmar et al., “Attention is all you need,” Proc. NeurIPS, vol. 30, 2017.
  42. Microsoft and Epic, “Alphasense,” https://research.alpha-sense.com, 2023.
  43. H. Cui, C. Wang, H. Maan et al., “scGPT: Towards building a foundation model for single-cell multi-omics using generative AI,” bioRxiv, pp. 2023–04, 2023.
  44. H. Du, Z. Li, D. Niyato, J. Kang, Z. Xiong, H. Huang, and S. Mao, “Diffusion-based reinforcement learning for edge-enabled AI-generated content services,” arXiv preprint arXiv:2303.13052, 2023.
  45. G. Tosato, C. M. Dalbagno, and F. Fumagalli, “EEG synthetic data generation using probabilistic diffusion models,” arXiv preprint arXiv:2303.06068, 2023.
  46. K. Gong, K. Johnson, G. El Fakhri et al., “PET image denoising based on denoising diffusion probabilistic model,” Eur. J. Nucl. Med. Mol. Imaging, pp. 1–11, 2023.
  47. X. Meng, Y. Gu, Y. Pan et al., “A novel unified conditional score-based generative framework for multi-modal medical image completion,” arXiv preprint arXiv:2207.03430, 2022.
  48. M. Hajij, G. Zamzmi, R. Paul et al., “Normalizing flow for synthetic medical images generation,” in Proc. HI-POCT, 2022, pp. 46–49.
  49. H. Du, J. Wang, D. Niyato, J. Kang, Z. Xiong, J. Zhang, and X. Shen, “Semantic communications for wireless sensing: RIS-aided encoding and self-supervised decoding,” IEEE J. Sel. Areas Commun., to appear, 2023.
  50. H. Du, J. Wang, D. Niyato et al., “AI-generated incentive mechanism and full-duplex semantic communications for information sharing,” IEEE J. Sel. Areas in Commun., vol. 41, no. 9, pp. 2981–2997, 2023.
  51. C. Liang, H. Du, Y. Sun et al., “Generative AI-driven semantic communication networks: Architecture, technologies and applications,” arXiv preprint arXiv:2401.00124, 2023.
  52. H. Du, G. Liu, D. Niyato et al., “Generative AI-aided joint training-free secure semantic communications via multi-modal prompts,” arXiv preprint arXiv:2309.02616, 2023.
  53. X. Wei, D. Wu, L. Zhou et al., “Cross-modal communication technology: A survey,” Fundam. Res., 2023.
  54. J. Corral-Acero, F. Margara, M. Marciniak et al., “The ‘digital twin’ to enable the vision of precision cardiology,” Eur. Heart J., vol. 41, no. 48, pp. 4556–4564, 2020.
  55. D. Hazra and Y.-C. Byun, “SynSigGAN: Generative adversarial networks for synthetic biomedical signal generation,” Biology, vol. 9, no. 12, p. 441, 2020.
  56. M. A. Pimentel, A. E. Johnson, P. H. Charlton et al., “Toward a robust estimation of respiratory rate from pulse oximeters,” IEEE Trans. Biomed. Eng., vol. 64, no. 8, pp. 1914–1923, 2016.
  57. J. M. L. Alcaraz and N. Strodthoff, “Diffusion-based conditional ECG generation with structured state space models,” Comput. Biol. Med., p. 107115, 2023.
  58. ——, “Diffusion-based time series imputation and forecasting with structured state space models,” Trans. Mach. Learn. Res., 2022.
  59. P. Wagner, N. Strodthoff, R.-D. Bousseljot et al., “PTB-XL, a large publicly available electrocardiography dataset,” Sci. data, vol. 7, no. 1, p. 154, 2020.
  60. P. A. Moghadam, S. Van Dalen, K. C. Martin et al., “A morphology focused diffusion probabilistic model for synthesis of histopathology images,” in Proc. IEEE/CVF CVPR, 2023, pp. 2000–2009.
  61. A. B. Levine, J. Peng, D. Farnell et al., “Synthesis of diagnostic quality cancer pathology images by generative adversarial networks,” J. pathol., vol. 252, no. 2, pp. 178–188, 2020.
  62. J. S. Yoon, C. Zhang, H.-I. Suk et al., “SADM: Sequence-aware diffusion model for longitudinal medical image generation,” in Proc. IPMI.   Springer, 2023, pp. 388–400.
  63. L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density estimation using real NVP,” arXiv preprint arXiv:1605.08803, 2016.
  64. R. Summers, “Nih chest x-ray dataset of 14 common thorax disease categories,” NIH Clinical Center: Bethesda, MD, USA, 2019.
  65. P. Tschandl, C. Rosendahl, and H. Kittler, “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions,” Sci. data, vol. 5, no. 1, pp. 1–9, 2018.
  66. Y. Du, R. Kips, A. Pumarola et al., “Avatars grow legs: Generating smooth human motion from sparse tracking inputs with diffusion model,” in Proc. IEEE/CVF CVPR, 2023, pp. 481–490.
  67. W. Yang, H. Du, Z. Q. Liew, W. Y. B. Lim, Z. Xiong, D. Niyato, X. Chi, X. S. Shen, and C. Miao, “Semantic communications for future Internet: Fundamentals, applications, and challenges,” IEEE Communications Surveys & Tutorials, 2023.
  68. H. Du, R. Zhang, D. Niyato, J. Kang, Z. Xiong, S. Cui, X. Shen, and D. I. Kim, “User-centric interactive AI for distributed diffusion model-based AI-generated content,” arXiv preprint arXiv:2311.11094, 2023.
  69. A. D. Raha, M. S. Munir, A. Adhikary et al., “Generative AI-driven semantic communication framework for NextG wireless network,” arXiv preprint arXiv:2310.09021, 2023.
  70. C. Zhang, D. Han, Y. Qiao et al., “Faster segment anything: Towards lightweight SAM for mobile applications,” arXiv preprint arXiv:2306.14289, 2023.
  71. E. Grassucci, S. Barbarossa, and D. Comminiello, “Generative semantic communication: Diffusion models beyond bit recovery,” arXiv preprint arXiv:2306.04321, 2023.
  72. H. Liu, D. Guo, X. Zhang et al., “Toward image-to-tactile cross-modal perception for visually impaired people,” IEEE Trans. Autom. Sci. Eng., vol. 18, no. 2, pp. 521–529, 2021.
  73. T. Kim, M. Cha, H. Kim et al., “Learning to discover cross-domain relations with generative adversarial networks,” in Proc. ICML.   PMLR, 2017, pp. 1857–1865.
  74. Y. Fang, X. Zhang, W. Xu et al., “Bidirectional visual-tactile cross-modal generation using latent feature space flow model,” Available at SSRN 4593113.
  75. A. Li, Y. Chen, S. Ni et al., “Haptic signal reconstruction in ehealth internet of things,” IEEE Internet Things J., vol. 9, no. 18, pp. 17 047–17 057, 2021.
  76. B. R. Barricelli, E. Casiraghi, J. Gliozzo et al., “Human digital twin for fitness management,” IEEE Access, vol. 8, pp. 26 637–26 664, 2020.
  77. S. Phung, A. Kumar, and J. Kim, “A deep learning technique for imputing missing healthcare data,” in Proc. IEEE EMBC.   IEEE, 2019, pp. 6513–6516.
  78. W. Dong, D. Y. T. Fong, J.-s. Yoon et al., “Generative adversarial networks for imputing missing data for big data clinical research,” BMC medical res. methodol., vol. 21, pp. 1–10, 2021.
  79. P. Hayati Rezvan, K. J. Lee, and J. A. Simpson, “The rise of multiple imputation: a review of the reporting and implementation of the method in medical research,” BMC med. res. methodol., vol. 15, pp. 1–14, 2015.
  80. D. J. Stekhoven and P. Bühlmann, “MissForest—non-parametric missing value imputation for mixed-type data,” Bioinformatics, vol. 28, no. 1, pp. 112–118, 2012.
  81. Q. Lyu and G. Wang, “Conversion between CT and mri images using diffusion and score-matching models,” arXiv preprint arXiv:2209.12104, 2022.
  82. C. Saharia, J. Ho, W. Chan et al., “Image super-resolution via iterative refinement,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 4, pp. 4713–4726, 2022.
  83. Y. Song, J. Sohl-Dickstein, D. P. Kingma et al., “Score-based generative modeling through stochastic differential equations,” Proc. ICLR, 2021.
  84. ——, “Score-based generative modeling through stochastic differential equations,” arXiv preprint arXiv:2011.13456, 2020.
  85. T. Nyholm, S. Svensson, S. Andersson et al., “MR and CT data with multiobserver delineations of organs in the pelvic area—part of the gold atlas project,” Med. phys., vol. 45, no. 3, pp. 1295–1300, 2018.
  86. I. Gulrajani, F. Ahmed, M. Arjovsky et al., “Improved training of wasserstein GANs,” Proc. NeurIPS, vol. 30, 2017.
  87. P. Huang, D. Li, Z. Jiao et al., “CoCa-GAN: common-feature-learning-based context-aware generative adversarial network for glioma grading,” in Proc. MICCAI.   Springer, 2019, pp. 155–163.
  88. T. Zhou, H. Fu, G. Chen et al., “Hi-Net: hybrid-fusion network for multi-modal mr image synthesis,” IEEE trans. med. imaging, vol. 39, no. 9, pp. 2772–2781, 2020.
  89. A. Sharma and G. Hamarneh, “Missing MRI pulse sequence synthesis using multi-modal generative adversarial network,” IEEE trans. med. imaging, vol. 39, no. 4, pp. 1170–1183, 2019.
  90. X. Liu, F. Xing, G. El Fakhri et al., “A unified conditional disentanglement framework for multimodal brain mr image translation,” in Proc. ISBI.   IEEE, 2021, pp. 10–14.
  91. A. Chartsias, T. Joyce, M. V. Giuffrida et al., “Multimodal MR synthesis via modality-invariant latent representation,” IEEE trans. med. imaging, vol. 37, no. 3, pp. 803–814, 2017.
  92. S. Kazeminia, C. Baur, A. Kuijper et al., “GANs for medical image analysis,” Artif. Intell. Med., vol. 109, p. 101938, 2020.
  93. H.-T. Chiang, Y.-Y. Hsieh, S.-W. Fu et al., “Noise reduction in ECG signals using fully convolutional denoising autoencoders,” IEEE Access, vol. 7, pp. 60 806–60 813, 2019.
  94. S. Nasrin, M. Z. Alom, R. Burada et al., “Medical image denoising with recurrent residual U-Net (R2U-Net) base auto-encoder,” in Proc. IEEE NAECON.   IEEE, 2019, pp. 345–350.
  95. P. Isola, J.-Y. Zhu, T. Zhou et al., “Image-to-image translation with conditional adversarial networks,” in Proc. CVPR, 2017, pp. 1125–1134.
  96. T. Xiang, M. Yurt, A. B. Syed et al., “DDM22{}^{2}start_FLOATSUPERSCRIPT 2 end_FLOATSUPERSCRIPT : Self-supervised diffusion mri denoising with generative diffusion models,” Proc. ICLR, 2023.
  97. A. Majeed, “Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health data,” J. King Saud Univ. - Comput. Inf. Sci., vol. 31, no. 4, pp. 426–435, 2019.
  98. A. Aminifar, F. Rabbi, V. K. I. Pun et al., “Diversity-aware anonymization for structured health data,” in Proc. IEEE EMBC.   IEEE, 2021, pp. 2148–2154.
  99. J. Yoon, L. N. Drumright, and M. van der Schaar, “Anonymization through data synthesis using generative adversarial networks (ADS-GAN),” IEEE J. Biomed. Health Inform., vol. 24, no. 8, pp. 2378–2388, 2020.
  100. E. Choi, S. Biswal, B. Malin et al., “Generating multi-label discrete patient records using generative adversarial networks,” in Proc. ML4H.   PMLR, 2017, pp. 286–305.
  101. L. Xie, K. Lin, S. Wang, F. Wang, and J. Zhou, “Differentially private generative adversarial network,” arXiv preprint arXiv:1802.06739, 2018.
  102. J. Jordon, J. Yoon, and M. Van Der Schaar, “PATE-GAN: Generating synthetic data with differential privacy guarantees,” in Proc. ICLR, 2018.
  103. J. Yoon, M. Mizrahi, N. F. Ghalaty et al., “EHR-Safe: generating high-fidelity and privacy-preserving synthetic electronic health records,” NPJ Digit. Med., vol. 6, no. 1, p. 141, 2023.
  104. M. Bordukova, N. Makarov, R. Rodriguez-Esteban et al., “Generative artificial intelligence empowers digital twins in drug discovery and clinical trials,” Expert Opin. Drug Discov., pp. 1–10, 2023.
  105. B. S. Center, “Alya Red: A computational heart,” https://www.bsc.es/news/bsc-news/alya-red-computational-heart, 2023.
  106. M. Lotfollahi, F. A. Wolf, and F. J. Theis, “scGen predicts single-cell perturbation responses,” Nat. Methods, vol. 16, no. 8, pp. 715–721, 2019.
  107. M. Lotfollahi, A. Klimovskaia Susmelj, C. De Donno et al., “Predicting cellular responses to complex perturbations in high-throughput screens,” Mol. Syst. Biol., p. e11517, 2023.
  108. R. M. Donovan-Maiye, J. M. Brown, C. K. Chan et al., “A deep generative model of 3D single-cell organization,” PLOS Comput. Biol., vol. 18, no. 1, p. e1009155, 2022.
  109. L. Jose, S. Liu, C. Russo et al., “Generative adversarial networks in digital pathology and histopathological image processing: A review,” J. Pathol. Inform., vol. 12, no. 1, p. 43, 2021.
  110. A. C. Quiros, R. Murray-Smith, and K. Yuan, “PathologyGAN: Learning deep representations of cancer tissue,” in Proc. PMLR, vol. 121.   PMLR, 06–08 Jul 2020, pp. 669–695.
  111. A. Brock, J. Donahue, and K. Simonyan, “Large scale GAN training for high fidelity natural image synthesis,” arXiv preprint arXiv:1809.11096, 2018.
  112. T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proc. IEEE/CVF CVPR, 2019, pp. 4401–4410.
  113. A. Jolicoeur-Martineau, “The relativistic discriminator: a key element missing from standard gan,” arXiv preprint arXiv:1807.00734, 2018.
  114. H. Ahmadian, P. Mageswaran, B. A. Walter et al., “Toward an artificial intelligence-assisted framework for reconstructing the digital twin of vertebra and predicting its fracture response,” Int. J. Numer. Method Biomed. Eng., vol. 38, no. 6, p. e3601, 2022.
  115. A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv preprint arXiv:1511.06434, 2015.
  116. X. Xing, J. Del Ser, Y. Wu et al., “HDL: Hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis,” IEEE J. Biomed. Health. Inform., 2022.
  117. R. Martinez-Velazquez, R. Gamez, and A. El Saddik, “Cardio twin: A digital twin of the human heart running on the edge,” in Proc. IEEE MeMeA.   IEEE, 2019, pp. 1–6.
  118. X. Li, Z. Gong, H. Yin et al., “A 3D deep supervised densely network for small organs of human temporal bone segmentation in CT images,” Neural Netw., vol. 124, pp. 75–85, 2020.
  119. N. J. Dhinagar, S. I. Thomopoulos, E. Laltoo et al., “Efficiently training vision transformers on structural MRI scans for alzheimer’s disease detection,” arXiv preprint arXiv:2303.08216, 2023.
  120. Y. Dong, M. Zhang, L. Qiu et al., “An arrhythmia classification model based on vision transformer with deformable attention,” Micromachines, vol. 14, no. 6, p. 1155, 2023.
  121. C. Che, P. Zhang, M. Zhu et al., “Constrained transformer network for ECG signal processing and arrhythmia classification,” BMC Med. Inform. Decis. Mak., vol. 21, no. 1, pp. 1–13, 2021.
  122. A. Rahman, J. M. J. Valanarasu, I. Hacihaliloglu et al., “Ambiguous medical image segmentation using diffusion models,” in Proc. IEEE/CVF CVPR, 2023, pp. 11 536–11 546.
  123. B. Kim, Y. Oh, and J. C. Ye, “Diffusion adversarial representation learning for self-supervised vessel segmentation,” Proc. ICLR, 2023.
  124. X. Guo, J. W. Gichoya, S. Purkayastha et al., “CVAD: An anomaly detector for medical images based on cascade VAE,” in Proc. MILLanD.   Springer, 2022, pp. 187–196.
  125. T. Nakao, S. Hanaoka, Y. Nomura et al., “Unsupervised deep anomaly detection in chest radiographs,” J. Digit. Imaging, vol. 34, pp. 418–427, 2021.
  126. J. Wolleb, F. Bieder, R. Sandkühler et al., “Diffusion models for medical anomaly detection,” in Proc. MICCAI.   Springer, 2022, pp. 35–45.
  127. J. Irvin, P. Rajpurkar, M. Ko et al., “Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,” in Proc. AAAI, vol. 33, no. 01, 2019, pp. 590–597.
  128. A. Johnson, L. Bulgarelli, T. Pollard et al., “MIMIC-IV,” PhysioNet, 2021.
  129. W. Hu and S. Y. Wang, “Predicting glaucoma progression requiring surgery using clinical free-text notes and transfer learning with transformers,” Transl. Vis. Sci. Techn., vol. 11, no. 3, pp. 37–37, 2022.
  130. J. Devlin, M.-W. Chang, K. Lee et al., “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
  131. J. Lee, W. Yoon, S. Kim et al., “BioBERT: a pre-trained biomedical language representation model for biomedical text mining,” Bioinformatics, vol. 36, no. 4, pp. 1234–1240, 2020.
  132. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “RoBERTa: A robustly optimized bert pretraining approach,” arXiv preprint arXiv:1907.11692, 2019.
  133. V. Sanh, L. Debut, J. Chaumond et al., “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019.
  134. Z. Wang, S. Stavrakis, and B. Yao, “Hierarchical deep learning with generative adversarial network for automatic cardiac diagnosis from ECG signals,” Comput. Biol. Med., vol. 155, p. 106641, 2023.
  135. A. Staffini, T. Svensson, U.-i. Chung et al., “A disentangled VAE-BiLSTM model for heart rate anomaly detection,” Bioengineering, vol. 10, no. 6, p. 683, 2023.
  136. S. Park, K. H. Lee, B. Ko et al., “Unsupervised anomaly detection with generative adversarial networks in mammography,” Sci. Rep., vol. 13, no. 1, p. 2925, 2023.
  137. C. Han, L. Rundo, K. Murao et al., “GAN-based multiple adjacent brain MRI slice reconstruction for unsupervised alzheimer’s disease diagnosis,” in Proc. CIBB.   Springer, 2019, pp. 44–54.
  138. T. N. Jarada, J. G. Rokne, and R. Alhajj, “SNF–CVAE: computational method to predict drug–disease interactions using similarity network fusion and collective variational autoencoder,” Knowl. Based Syst., vol. 212, p. 106585, 2021.
  139. F. Xu, S. Liu, Y. Xiang et al., “Prediction of the short-term therapeutic effect of anti-VEGF therapy for diabetic macular edema using a generative adversarial network with OCT images,” J. Clin. Med., vol. 11, no. 10, p. 2878, 2022.
  140. T.-C. Wang, M.-Y. Liu, J.-Y. Zhu et al., “High-resolution image synthesis and semantic manipulation with conditional gans,” in Proc. IEEE CVPR, 2018, pp. 8798–8807.
  141. C. Mennella, U. Maniscalco, G. De Pietro et al., “Generating a novel synthetic dataset for rehabilitation exercises using pose-guided conditioned diffusion models: A quantitative and qualitative evaluation,” Comput. Biol. Med., vol. 167, p. 107665, 2023.
  142. L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proc. IEEE/CVF ICCV, 2023, pp. 3836–3847.
  143. I. Boukhennoufa, D. Jarchi, X. Zhai et al., “A novel model to generate heterogeneous and realistic time-series data for post-stroke rehabilitation assessment,” IEEE Trans. Neural Syst. Rehabilitation Eng., vol. 31, pp. 2676–2687, 2023.
  144. X. Du, Y. Liu, Z. Lu et al., “A low-latency communication design for brain simulations,” IEEE Netw., vol. 36, no. 2, pp. 8–15, 2022.
  145. W. Lu et al., “The human digital twin brain in the resting state and in action,” arXiv preprint arXiv:2211.15963, 2022.
Citations (10)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets