[1] Travis W D, Brambilla E, Noguchi M et al. International association for the study of lung cancer/American thoracic society/European respiratory society international multidisciplinary classification of lung adenocarcinoma: executive summary[J]. Journal of Thoracic Oncology, 8, 381-385(2011).

[2] Müller M, Gromicho M, de Carvalho M et al. Explainable models of disease progression in ALS: learning from longitudinal clinical data with recurrent neural networks and deep model explanation[J]. Computer Methods and Programs in Biomedicine Update, 1, 100018(2021).

[3] Di S H, Yang W H, Liao M et al. Liver tumor segmentation from CT images based on RA-Unet[J]. Chinese Journal of Scientific Instrument, 43, 65-72(2022).

[4] He F F, Zhang Q, Yang C et al. Application of image segmentation methods based on deep learning in aortic diseases[J]. Journal of Clinical Cardiology, 38, 449-454(2022).

[5] Liu Q D, Chen C, Qin J et al. FedDG: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space[C], 1013-1023(2021).

[6] Sheller M J, Reina G A, Edwards B et al. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation[M]. Crimi A, Bakas S, Kuijf H, et al. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Lecture notes in computer science, 11383, 92-104(2019).

[7] McMahan H B, Moore E, Ramage D et al. Communication-efficient learning of deep networks from decentralized data[EB/OL]. https://arxiv.org/abs/1602.05629

[8] Sahu A K, Li T, Sanjabi M et al. Federated optimization in heterogeneous networks[EB/OL]. https://arxiv.org/pdf/1812.06127.pdf

[9] Li Q B, He B S, Song D. Model-contrastive federated learning[C], 10708-10717(2021).

[10] Jiang M R, Wang Z R, Dou Q. HarmoFL: harmonizing local and global drifts in federated learning on heterogeneous medical images[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 36, 1087-1095(2022).

[11] Hsieh K, Phanishayee A, Mutlu O et al. The non-IID data quagmire of decentralized machine learning[EB/OL]. https://arxiv.org/abs/1910.00189

[12] Zhang Z H, Li Q D, Fu Y et al. Adaptive federated deep learning algorithm for Non-IID data[J/OL]. Acta Automatica Sinica, 1-13. https://doi.org/10.16383/j.aas.c201018

[13] Chen H, Zhang T, Chen R B. Image classification method based on lightweight convolutional transformer and its application in remote sensing image classification[J/OL]. Journal of Electronics & Information Technology, 1-9(2022). https://kns.cnki.net/kcms/detail/11.4494.TN.20220705.1638.014.html

[14] McMahan B, Moore E, Ramage D et al. Communication-efficient learning of deep networks from decentralized data[EB/OL]. https://arxiv.org/abs/1602.05629

[15] Schlemper J, Oktay O, Schaap M et al. Attention gated networks: learning to leverage salient regions in medical images[J]. Medical Image Analysis, 53, 197-207(2019).

[16] Srinivas A, Lin T Y, Parmar N et al. Bottleneck transformers for visual recognition[EB/OL]. https://arxiv.org/abs/2101.11605

[17] Yang M, Zhang Y X, Wang X Z et al. Multi-instance ensemble learning with discriminative bags[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52, 5456-5467(2022).

[18] Khellal A, Ma H B, Fei Q. Convolutional neural network based on extreme learning machine for maritime ships recognition in infrared images[J]. Sensors, 18, 1490(2018).

[19] Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 70, 489-501(2006).

[20] Shafahi A, Najibi M, Ghiasi A et al. Adversarial training for free![EB/OL]. https://arxiv.org/abs/1904.12843

[21] Pan J, Pham V, Dorairaj M et al. Adversarial validation approach to concept drift problem in user targeting automation systems at uber[EB/OL]. https://arxiv.org/abs/2004.03045