The Frontiers of Society, Science and Technology, 2023, 5(3); doi: 10.25236/FSST.2023.050312.
Hang Yi, Tongxuan Bie, Tongjiang Yan
College of Science, China University of Petroleum (East China), Tsingtao, China
As the Internet grows in popularity, more and more classification jobs, such as IoT, finance industry and healthcare field, rely on mobile edge computing to advance machine learning. In the medical industry, however, good diagnostic accuracy necessitates the combination of large amounts of labeled data to train the model, which is difficult and expensive to collect and risks jeopardizing patients' privacy. In this paper, we offer a novel medical diagnostic framework that employs a federated learning platform to ensure patient data privacy by transferring classification algorithms acquired in a labeled domain to a domain with sparse or missing labeled data. Rather than using a generative adversarial network, our framework uses a discriminative model to build multiple classification loss functions with the goal of improving diagnostic accuracy. It also avoids the difficulty of collecting large amounts of labeled data or the high cost of generating large amount of sample data. Experiments on real-world image datasets demonstrates that the suggested adversarial federated transfer learning method is promising for real-world medical diagnosis applications that use image classification.
Federated transfer learning, Medical diagnosis, Adaptation approaches, Data privacy, Domain shift
Hang Yi, Tongxuan Bie, Tongjiang Yan. Framework Construction of an Adversarial Federated Transfer Learning Classifier. The Frontiers of Society, Science and Technology (2023) Vol. 5, Issue 3: 64-75. https://doi.org/10.25236/FSST.2023.050312.
[1] Maganioti, A.E., Chrissanthi, H.D., Charalabos, P.C., Andreas, R.D., George, P.N. and Christos, C.N. Cointegration of Event-Related Potential (ERP) Signals in Experiments with Different Electromagnetic Field (EMF) Conditions. Health, (2010) 2, 400-406.
[2] Bootorabi, F., Haapasalo, J., Smith, E., Haapasalo, H. and Parkkila, S. Carbonic Anhydrase VII—A Potential Prognostic Marker in Gliomas. Health, (2011) 3, 6-12.
[3] Abbas A, Abdelsamea MM, Gaber MM. 4S-DT: Self-supervised Super Sample Decomposition for Transfer Learning with Application to COVID-19 Detection, IEEE Trans Neural Netw Learn Syst. 2021 Jul; 32(7):2798-2808. doi: 10.1109/TNNLS.2021.3082015. Epub 2021 Jul 6. PMID: 34038371.
[4] K.Bu, Y.He, X.Jing and J.Han. Adversarial Transfer Learning for Deep Learning Based Automatic Modulation Classification, IEEE Signal Processing Letters, vol.27, pp.880-884, 2020, doi: 10.1109/LSP.2020.2991875.
[5] P.Chen, L.Li, Q.Wu and J.Wu. SPIQ: A Self-supervised Pre-Trained Model for Image Quality Assessment, IEEE Signal Processing Letters, vol. 29, pp.513-517, 2022, doi: 10.1109/LSP.2022.3145326.
[6] Y. S. Cho, S. Kim and J. H. Lee. Source model selection for transfer learning of image classification using supervised contrastive loss, 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), 2021, pp. 325-329, doi: 10.1109/BigComp51126.2021.00070.
[7] DiSpirito A, Li D, Vu T, Chen M, Zhang D, Luo J, Horstmeyer R, Yao J. Reconstructing Undersampled Photoacoustic Microscopy Images using Deep Learning, IEEE Trans Med Imaging, 2021, vol. 40, pp. 562-570. doi:10.1109/TMI.2020.3031541.
[8] J. Fan, J. H. Lee and Y. Lee. Application of Transfer Learning for Image Classification on Dataset with not Mutually Exclusive Classes, 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2021, pp.1-4, doi:10.1109/ITC-CSCC52171. 2021.9501424.
[9] Z. Fan, L. Shi, Q. Liu, Z. Li and Z. Zhang. Discriminative Fisher Embedding Dictionary Transfer Learning for Object Recognition, IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS. 2021.3089566.
[10] Goodfellow, Ian, et al. Generative Adversarial Nets, Proceedings of the International Conference on Neural Information Processing Systems, 2014, pp. 2672–2680.
[11] Joaquin Quiñonero-Candela; Masashi Sugiyama; Anton Schwaighofer; Neil D. Lawrence. Covariate Shift by Kernel Mean Matching, in Dataset Shift in Machine Learning, MIT Press, 2009, pp. 131-160.
[12] E. T. Hastuti, A. Bustamam, P. Anki, R. Amalia and A. Salma. Performance of True Transfer Learning using CNN DenseNet121 for COVID-19 Detection from Chest X-Ray Images, 2021 IEEE International Conference on Health, Instrumentation & Measurement, and Natural Sciences (InHeNce), 2021, pp. 1-5, doi: 10.1109/InHeNce52833.2021.9537261.
[13] Z. Hu, T. Li, Y. Yang, X. Liu, H. Zheng and D. Liang. Super-resolution PET Image Reconstruction with Sparse Representation, 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), 2017, pp. 1-3, doi: 10.1109/NSSMIC.2017.8532893.
[14] Lei, Yaguo, et al. Applications of Machine Learning to Machine Fault Diagnosis: A Review and Roadmap. Mechanical Systems and Signal Processing 138 (2020): 106587.
[15] F. Liu, H. Ding, D. Li, T. Wang, Z. Luo and L. Chen. Few-shot Learning with Data Enhancement and Transfer Learning for Underwater Target Recognition, 2021 OES China Ocean Acoustics (COA), 2021, pp. 992-994, doi: 10.1109/COA50123.2021.9519853.
[16] Y. Liu, Y. Kang, C. Xing, T. Chen and Q. Yang. A Secure Federated Transfer Learning Framework, in IEEE Intelligent Systems, vol. 35, no. 4, pp. 70-82, 1 July-Aug. 2020, doi: 10.1109/MIS.2020.2988525.
[17] Z. Liu et al. Remove Appearance Shift for Ultrasound Image Segmentation via Fast and Universal Style Transfer, 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 2020, pp. 1824-1828, doi: 10.1109/ISBI45749.2020.9098457.
[18] S. A. H. Minoofam, A. Bastanfard and M. R. Keyvanpour. TRCLA: A Transfer Learning Approach to Reduce Negative Transfer for Cellular Learning Automata, in IEEE Transactions on Neural Networks and Learning Systems,2021, vol. pp, doi :10.1109/TNNLS.2021.3106705.
[19] A. Nehvi, R. Dar and A. Assad. Visual Recognition of Local Kashmiri Objects with Limited Image Data using Transfer Learning, 2021 International Conference on Emerging Techniques in Computational Intelligence (ICETCI), 2021, pp. 49-52, doi: 10.1109/ICETCI51973.2021.9574047.
[20] O. Oktay et al. Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation, in IEEE Transactions on Medical Imaging, vol. 37, no. 2, pp. 384-395, Feb. 2018, doi: 10.1109/TMI.2017.2743464.
[21] Peng, Xingchao, et al. Federated Adversarial Domain Adaptation, arXiv preprint arXiv: 1911. 02054 (2019).
[22] V. Perumal and K. Theivanithy. A Transfer Learning Model for COVID-19 Detection with Computed Tomography and Sonogram Images, 2021 Sixth International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2021, pp. 80-83, doi: 10.1109/WiSPNET51692. 2021. 9419419.
[23] Sabuncu MR, Yeo BT, Van Leemput K, Fischl B, Golland P. A Generative Model for Image Segmentation based on Label Fusion, IEEE Trans Med Imaging, 2010, vol. 29, pp. 1714-29, doi: 10.1109/TMI.2010.2050897. Epub 2010 Jun 17. PMID: 20562040; PMCID: PMC3268159.
[24] J. Salamon and J. P. Bello. Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification, in IEEE Signal Processing Letters, vol. 24, no. 3, pp. 279-283, March 2017, doi: 10.1109/LSP.2017.2657381.
[25] S. Sharma, C. Xing, Y. Liu and Y. Kang. Secure and Efficient Federated Transfer Learning, 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 2569-2576, doi: 10.1109/BigData47090.2019.9006280.
[26] M. F. I. Soumik, A. Z. B. Aziz and M. A. Hossain. Improved Transfer Learning based Deep Learning Model for Breast Cancer Histopathological Image Classification, 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), 2021, pp. 1-4, doi: 10.1109/ ACMI53878.2021.9528263.
[27] Sun, Baochen, and Kate Saenko. Deep Coral: Correlation Alignment for Deep Domain Adaptation. European Conference on Computer Vision. Springer, Cham, 2016, pp. 443-450.
[28] D. Tellez, G. Litjens, J. van der Laak and F. Ciompi. Neural Image Compression for Gigapixel Histopathology Image Analysis, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 2, pp. 567-578, 1 Feb. 2021, doi: 10.1109/TPAMI.2019.2936841.
[29] A. Van Opbroek, H. C. Achterberg, M. W. Vernooij and M. De Bruijne. Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning, in IEEE Transactions on Medical Imaging, vol. 38, no. 1, pp. 213-224, Jan. 2019, doi: 10.1109/TMI.2018.2859478.
[30] F. G. Veshki and S. A. Vorobyov. Efficient ADMM-based Algorithms for Convolutional Sparse Coding, in IEEE Signal Processing Letters, vol. 29, pp. 389-393, 2022, doi: 10.1109/LSP.2021.3135196.
[31] Wu, Lin, Teng Wang, and Changyin Sun. Multi-modal Visual Place Recognition in Dynamics-Invariant Perception Space. IEEE Signal Processing Letters 28 (2021): 2197-2201.
[32] Q. Xu, L. Wang, Y. Wang, W. Sheng and X. Deng. Deep Bilateral Learning for Stereo Image Super-Resolution, in IEEE Signal Processing Letters, vol. 28, pp. 613-617, 2021, doi: 10.1109/LSP. 2021.3066125.
[33] Yang G, Gu J, Chen Y, Liu W, Tang L, Shu H, Toumoulin C. Automatic Kidney Segmentation in CT Images Based on Multi-atlas Image Registration. Annu Int Conf IEEE Eng Med Biol Soc. 2014; 2014:5538-41. doi: 10.1109/EMBC.2014.6944881. PMID: 25571249.
[34] W. Zhang and X. Li. Federated Transfer Learning for Intelligent Fault Diagnostics using Deep Adversarial Networks with Data Privacy, in IEEE/ASME Transactions on Mechatronics, vol. 27, no. 1, pp. 430-439, Feb. 2022, doi: 10.1109/TMECH.2021.3065522.
[35] Zhang, Wei, et al. Machinery Fault Diagnosis with Imbalanced Data using Deep Generative Adversarial Networks. Measurement 152 (2020): 107377.
[36] Zhang, Wei et al. Federated Learning for Machinery Fault Diagnosis with Dynamic Validation and Self-supervision. Knowl. Based Syst. 213 (2021): 106679.
[37] X. Zhang, F. Wang and H. Li. An Efficient Method for Cooperative Multi-target Localization in Automotive Radar, in IEEE Signal Processing Letters, vol. 29, pp. 16-20, 2022, doi: 10.1109/LSP.2021.3121626.
[38] E. Tjoa and C. Guan. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 11, pp. 4793-4813, Nov. 2021, doi: 10.1109/TNNLS.2020.3027314.