Abstract
Public multimedia datasets can enhance knowledge discovery and model development as more researchers have the opportunity to contribute to exploring them. However, as these datasets become larger and more multimodal, besides analysis, efficient storage and sharing can become a challenge. Furthermore, there are inherent privacy risks when publishing any data containing sensitive information about the participants, especially when combining different data sources leading to unknown discoveries. Proposed solutions include standard methods for anonymization and new approaches that use generative models to produce fake data that can be used in place of real data. However, there are many open questions regarding whether these generative models hold information about the data used to train them and if this information could be retrieved, making them not as privacy-preserving as one may think. This paper reviews some important milestones that the research community has reached so far in important challenges in multimedia data analysis. In addition, we discuss the long-term and short-term challenges associated with publishing open multimedia datasets, including questions regarding efficient sharing, data modeling, and ensuring that the data is appropriately anonymized.
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References
Barata, C., et al.: A survey of feature extraction in dermoscopy image analysis of skin cancer. IEEE J. Biomed. Health Inform. 23(3), 1096–1109 (2018)
Bochare, A., et al.: Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer. Int. J. Med. Eng. Inform. 6(2), 87–99 (2014). https://doi.org/10.1504/IJMEI.2014.060245
Dwork, C., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)
Goyal, C.: Data masking: need, techniques & solutions. Int. Res. J. Manag. Sci. Technol. (IRJMST) 6(5), 221–229 (2015)
Gui, J., et al.: A review on generative adversarial networks: algorithms, theory, and applications. IEEE Trans. Knowl. Data Eng. (2021)
Narayanan, A., et al.: Robust de-anonymization of large sparse datasets (2008). https://doi.org/10.1109/SP.2008.33
Nguyen, N.D., et al.: Varmole: a biologically drop-connect deep neural network model for prioritizing disease risk variants and genes. Bioinformatics 37(12), 1772–1775 (2021)
Nguyen, T., et al.: Combining datasets to increase the number of samples and improve model fitting (2022). https://doi.org/10.48550/ARXIV.2210.05165
Nguyen, T., et al.: DPER: direct parameter estimation for randomly missing data. Knowl. Based Syst. 240, 108082 (2022)
Nguyen, T., et al.: Principle Components Analysis based frameworks for efficient missing data imputation algorithms. arXiv preprint arXiv:2205.15150 (2022)
Pathak, D., et al.: Constrained convolutional neural networks for weakly supervised segmentation (2015)
Saldanha, O.L., et al.: Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat. Med. 28(6), 1232–1239 (2022). https://doi.org/10.1038/s41591-022-01768-5
Sohl-Dickstein, J., et al.: Deep unsupervised learning using nonequilibrium thermodynamics (2015)
Thambawita, V., et al.: DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine. Sci. Rep. 11(1), 21896 (2021)
Thambawita, V., et al.: DeepSynthBody: the beginning of the end for data deficiency in medicine (2021). https://doi.org/10.1109/ICAPAI49758.2021.9462062
Tjoa, E., et al.: A survey on explainable artificial intelligence (XAI): toward medical XAI. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 4793–4813 (2021). https://doi.org/10.1109/TNNLS.2020.3027314
Tu, S.N.T., et al.: FinNet: solving time-independent differential equations with finite difference neural network. arXiv:2202.09282
Ulas, C., Tetteh, G., Kaczmarz, S., Preibisch, C., Menze, B.H.: DeepASL: kinetic model incorporated loss for denoising arterial spin labeled MRI via deep residual learning. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11070, pp. 30–38. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00928-1_4
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Nguyen, T., Storås, A.M., Thambawita, V., Hicks, S.A., Halvorsen, P., Riegler, M.A. (2023). Multimedia Datasets: Challenges and Future Possibilities. In: Dang-Nguyen, DT., et al. MultiMedia Modeling. MMM 2023. Lecture Notes in Computer Science, vol 13834. Springer, Cham. https://doi.org/10.1007/978-3-031-27818-1_58
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