[1] [1] YU Z, HAN S. 3D medical image segmentation based on multi-scale MPU-Net[EB/OL]. (2023-07-11)[2023-10-12]. https://arxiv.org/abs/2307.05799.

[2] [2] LIU T, LU Y, ZHANG Y, et al. A bone segmentation method based on multi-scale features fuse U2Net and improved Dice loss in CT image process[J]. Biomedical signal processing and control, 2022, 77: 103813.

[3] [3] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 8-10, 2015, Boston, MA, USA. New York: IEEE, 2015: 3431-3440.

[4] [4] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015), October 5-9, 2015, Munich, Germany. Heidelberg: Springer International Publishing, 2015: 234-241.

[5] [5] OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention U-Net: learning where to look for the pancreas[EB/OL]. (2018-04-11) [2023-10-12]. https://arxiv.org/abs/1804.03999.

[6] [6] JHA D, SMEDSRUD P H, RIEGLER M A, et al. Resunet++: an advanced architecture for medical image segmentation[C]//2019 IEEE International Symposium on Multimedia (ISM), December 9-11, 2019, San Diego, CA, USA. New York: IEEE, 2019: 2225-2255.

[7] [7] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7132-7141.

[8] [8] WANG Y, LIANG B, DING M, et al. Dense semantic labeling with atrous spatial pyramid pooling and decoder for high-resolution remote sensing imagery[J]. Remote sensing, 2018, 11(1): 20.

[9] [9] HUANG H, LIN L, TONG R, et al. Unet 3+: a full-scale connected Unet for medical image segmentation[C]//2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 4-8, 2020, Virtual. New York: IEEE, 2020: 1055-1059.

[10] [10] KATTENBORN T, LEITLOFF J, SCHIEFER F, et al. Review on convolutional neural networks (CNN) in vegetation remote sensing[J]. ISPRS journal of photo-grammetry and remote sensing, 2021, 173: 24-49.

[11] [11] CHEN J, LU Y, YU Q, et al. Transunet: transformers make strong encoders for medical image segmentation[EB/OL]. (2021-02-08) [2023-10-12]. https://arxiv.org/abs/2102.04306.

[12] [12] TAUD H, MAS J F. Multilayer perceptron (MLP)[J]. Geomatic approaches for modeling land change scenarios, 2018: 451-455.

[13] [13] VALANARASU J M J, PATEL V M. Unext: MLP-based rapid medical image segmentation network[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022), September 18-22, 2022, Singapore. Cham: Springer Nature Switzerland, 2022: 23-33.

[14] [14] YU J, GAO H, CHEN Y, et al. Deep object detector with attentional spatiotemporal LSTM for space human-robot interaction[J]. IEEE transactions on human-machine systems, 2022, 52(4): 784-793.

[15] [15] YU J, GAO H, ZHOU D, et al. Deep temporal model-based identity-aware hand detection for space human-robot interaction[J]. IEEE transactions on cybernetics, 2021, 52(12): 13738-13751.

[16] [16] ZHU F, CUI J, ZHU B, et al. Semantic segmentation of urban street scene images based on improved U-Net network[J]. Optoelectronics letters, 2023, 19(3): 179-185.

[17] [17] IEK , ABDULKADIR A, LIENKAMP S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[C]//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), October 17-21, 2016, Athens, Greece. Heidelberg: Springer International Publishing, 2016: 424-432.

[18] [18] MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]//2016 4th International Conference on 3D Vision (3DV), October 25-28, 2016, Stanford, USA. New York: IEEE, 2016: 565-571.

[19] [19] LI J, CHEN Z, ZHONG Y, et al. Appearance-based gaze estimation for ASD diagnosis[J]. IEEE transactions on cybernetics, 2022, 52(7): 6504-6517.

[20] [20] ZHU E R, ZHAO H C, HU A F. Semi-supervised cardiac MRI image of the left ventricle segmentation algorithm based on contrastive learning[J]. Optoelectronics letters, 2022, 18(9): 547-552.

[21] [21] HENDRYX S M, LEACH A B, HEIN P D, et al. Meta-learning initializations for image segmentation[EB/OL]. (2019-12-13) [2023-10-12]. https://arxiv.org/abs/1912.06290.

[22] [22] SHEN Q, LI Y, JIN J, et al. Q-net: query-informed few-shot medical image segmentation[EB/OL]. (2022-08-24)[2023-10-12]. https://arxiv.org/abs/2208.11451.

[23] [23] LIU Z, LIN W, LI X, et al. ADNet: attention-guided deformable convolutional network for high dynamic range imaging[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 463-470.

[24] [24] HOWARD A G, ZHU M, CHEN B, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17) [2023-10-12]. https://arxiv.org/abs/1704.04861.

[25] [25] ZHANG X, ZHOU X, LIN M, et al. Shufflenet: an extremely efficient convolutional neural network for mobile devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 6848-6856.

[26] [26] DAI Y, GIESEKE F, OEHMCKE S, et al. Attentional feature fusion[C]//Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, June 20-25, 2021, Nashville, TN, USA. New York: IEEE, 2021: 3560-3569.

[27] [27] XU J, ZHAO Y, XU F. RDPNet: a single-path light-weight CNN with re-parameterization for CPU-type edge devices[J]. Journal of cloud computing, 2022, 11(1): 54.

[28] [28] ZHONG S, WEN W, QIN J. Switchable self-attention module[EB/OL]. (2022-09-13) [2023-10-12]. https://arxiv.org/abs/2209.05680.

[29] [29] ZHUANG X. Multivariate mixture model for cardiac segmentation from multi-sequence MRI[C]//Medical Image Computing and Computer-Assisted Intervention (MICCAI 2016), October 17-21, 2016, Athens, Greece. Heidelberg: Springer International Publishing, 2016: 581-588.

[30] [30] KAVUR A E, GEZER N S, BARI M, et al. CHAOS challenge-combined (CT-MR) healthy abdominal organ segmentation[J]. Medical image analysis, 2021, 69: 101950.