Abstract
Deep learning models have achieved state-of-the-art in many challenging domains, whereas it is a data-hungry method. Collecting sensitive and labelled medical data sets is challenging and costly. Recently, federated learning has been used to train a model without sharing the data at a central place for a single task. We propose a novel Multi-task federated learning (MTFL) approach to utilize the data sets of various similar kinds of tasks. We used two binary class X-ray data sets: Pneumonia disease classification and TB disease classification. We compared MTFL with federated learning for a single task and CNN with data in one place. Results show that MTFL has achieved better specificity and accuracy than other models.
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References
Abdulrahman, S., Tout, H., Ould-Slimane, H., Mourad, A., Talhi, C., Guizani, M.: A survey on federated learning: the journey from centralized to distributed on-site learning and beyond. IEEE Internet Things J. 8(7), 5476–5497 (2021). https://doi.org/10.1109/JIOT.2020.3030072
Argyriou, A., Evgeniou, T., Pontil, M.: Multi-task feature learning. In: Advances in Neural Information Processing Systems, vol. 19 (2006)
Beguier, C., Terrail, J.O.d., Meah, I., Andreux, M., Tramel, E.W.: Differentially private federated learning for cancer prediction. arXiv preprint arXiv:2101.02997 (2021)
Brisimi, T.S., Chen, R., Mela, T., Olshevsky, A., Paschalidis, I.C., Shi, W.: Federated learning of predictive models from federated electronic health records. Int. J. Med. Inform. 112, 59–67 (2018)
Guan, Q., Huang, Y., Zhong, Z., Zheng, Z., Zheng, L., Yang, Y.: Diagnose like a radiologist: attention guided convolutional neural network for thorax disease classification. arXiv preprint arXiv:1801.09927 (2018)
Hesamian, M.H., Jia, W., He, X., Kennedy, P.: Deep learning techniques for medical image segmentation: achievements and challenges. J. Digit. Imaging 32(4), 582–596 (2019)
Hua, K.L., Hsu, C.H., Hidayati, S.C., Cheng, W.H., Chen, Y.J.: Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Therapy 8 (2015)
Islam, M.T., Aowal, M.A., Minhaz, A.T., Ashraf, K.: Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv preprint arXiv:1705.09850 (2017)
Kumar, P., Grewal, M., Srivastava, M.M.: Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds.) ICIAR 2018. LNCS, vol. 10882, pp. 546–552. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93000-8_62
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284(2), 574–582 (2017)
Maier, A., Syben, C., Lasser, T., Riess, C.: A gentle introduction to deep learning in medical image processing. Z. Med. Phys. 29(2), 86–101 (2019)
McMahan, B., Moore, E., Ramage, D., Hampson, S., Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial intelligence and statistics, pp. 1273–1282. PMLR (2017)
Mooney, P.: Chest x-ray images (pneumonia) (2021). https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia
Nafisah, S.I., Muhammad, G.: Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence. Neural Comput. Appl. 1–21 (2022)
Pfitzner, B., Steckhan, N., Arnrich, B.: Federated learning in a medical context: a systematic literature review. ACM Trans. Internet Technol. (TOIT) 21(2), 1–31 (2021)
Rajpurkar, P., et al.: Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv:1711.05225 (2017)
Rieke, N., et al.: The future of digital health with federated learning. NPJ Digit. Med. 3(1), 1–7 (2020)
Ruder, S.: An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098 (2017)
Singh, P.P., Prasad, S., Chaudhary, A.K., Patel, C.K., Debnath, M.: Classification of effusion and cartilage erosion affects in osteoarthritis knee MRI images using deep learning model. In: Nain, N., Vipparthi, S.K., Raman, B. (eds.) CVIP 2019. CCIS, vol. 1148, pp. 373–383. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-4018-9_34
Standley, T., Zamir, A.R., Chen, D., Guibas, L., Malik, J., Savarese, S.: Which tasks should be learned together in multi-task learning? (2019). https://doi.org/10.48550/ARXIV.1905.07553, https://arxiv.org/abs/1905.07553
Tawsifur Rahman, Muhammad Chowdhury, A.K.: Tuberculosis (tb) chest x-ray database (2021). https://www.kaggle.com/datasets/tawsifurrahman/tuberculosis-tb-chest-xray-dataset
Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5(1), 1–19 (2021)
Zhang, C., Xie, Y., Bai, H., Yu, B., Li, W., Gao, Y.: A survey on federated learning. Knowl.-Based Syst. 216, 106775 (2021). https://doi.org/10.1016/j.knosys.2021.106775, https://www.sciencedirect.com/science/article/pii/S0950705121000381
Zhang, Y., Yang, Q.: A survey on multi-task learning. IEEE Trans. Knowl. Data Eng. 1 (2021). https://doi.org/10.1109/TKDE.2021.3070203
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Kumar, P., Sinha, I.K., Singh, K.P. (2023). MTFL: Multi-task Federated Learning for Classification of Healthcare X-Ray Images. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_43
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