[1] DALCA A V, GUTTAG J, SABUNCU M R. Anatomical priors in convolutional networks for unsupervised biomedical segmentation[C], 9290-9299(2018).

[2] ZHOU T, LU H, YANG Z et al. The ensemble deep learning model for novel COVID-19 on CT images[J]. Applied Soft Computing, 98, 106885(2021).

[3] ZHOU Tao, DANG Pei, LU Huiling et al. Multi-modal, multi-scale and multi-dimension PET/CT image Transformer segmentation model[J]. Journal of Electronics and Information Technology, 45, 3529-3537(2023).

[4] LI L, ZHAO X, LU W et al. Deep learning for variational multimodality tumor segmentation in PET/CT[J]. Neurocomputing, 392, 277-295(2020).

[5] FU X, BI L, KUMAR A et al. Multimodal spatial attention module for targeting multimodal PET-CT lung tumor segmentation[J]. IEEE Journal of Biomedical and Health Informatics, 25, 3507-3516(2021).

[6] CHEN S, LI A, CHEN J et al. Hybrid attention fusion segmentation network for diffuse large B-cell lymphoma in PET-CT[C], 72-76(2022).

[7] ZHOU X, SUN Y, DENG M et al. Robust semi-supervised multimodal medical image segmentation via cross modality collaboration[C], 57-67(2024).

[8] ZHOU S, NIE D, ADELI E et al. High-resolution encoder-decoder networks for low-contrast medical image segmentation[J]. IEEE Transactions on Image Processing, 29, 461-475(2019).

[9] ZHOU P, LI Y, CHEN H et al. Coco-attention for tumor segmentation in weakly paired multimodal MRI Images[J]. IEEE Journal of Biomedical and Health Informatics, 27, 2944-2955(2023).

[10] ZHOU Tao, DONG Yali, LIU Shan et al. Cross-modality multi-encoder hybrid attention U-net for lung tumors images segmentation[J]. Acta Photonica Sinica, 51, 0410006(2022).

[11] BOLYA D, ZHOU C, XIAO F et al. Yolact: real-time instance segmentation[C], 9157-9166(2019).

[12] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 2117-2125(2017).

[13] WANG S, LI B Z, KHABSA M et al. Linformer: Self-attention with linear complexity[J/OL]. arXiv, 04768(2006). https://doi.org/10.48550/arXiv.2006.04768

[14] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[15] WOO S, PARK J, LEE J Y et al. Cbam: convolutional block attention module[C], 3-19(2018).

[16] ZHOU Tao, ZHAO Yanan, LU Huiling et al. Case segmentation of medical image: from candidate region to non-candidate region[J]. Journal of Biomedical Engineering, 39, 1218-1232(2022).

[17] HE K, ZHANG X, REN S et al. Deep residual learning for image recognition[C], 770-778(2016).

[18] ZHOU C[M]. Yolact++ better real-time instance segmentation(2020).

[19] HE K, GKIOXARI G, DOLL´AR P et al. Mask R-CNN[C], 2961-2969(2017).

[20] LIANG Xinyu, LIN Xikun, QUAN Jichuan et al. Research progress of image instance segmentation based on deep learning[J]. Acta Electronica Sinica, 48, 2476(2020).