[1] M A PYNNONEN. Clinical practice guideline： Evaluation of the neck mass in adults. Otolaryngol. Head Neck Surg., 157, 355(2017).

[2] H DILLEKÅS, M S ROGERS, O STRAUME. Are 90% of deaths from cancer caused by metastases？. Cancer Medicine, 8, 5574-5576(2019).

[3] S LEE, M CHO, W LEE et al. Predicting lymph node metastasis and distant metastasis using differential correlations of miRNAs and their target RNAs in cancer, 457-463(2022).

[4] D OYAMA, Y ADACHI, N TSUYUGUCHI. Magnetic resonance imaging using a magnetoresistive sensor with a flux transformer. IEEE Transactions on Magnetics, 59, 5100505(2023).

[5] Q WANG, K QIAO, R X QIN et al. Predictive model of breast cancer lymph node metastasis based on deep learning （E-Transformer）, 168-173(2021).

[6] [6] 周涛， 党培， 陆惠玲， 等. 跨模态跨尺度跨维度的PET/CT图像的Transformer分割模型［J/OL］. 电子与信息学报， 2022： 1-9. （2022-12-13）. https：//kns.cnki.net/kcms/detail/11.4494.TN.202212 13.1157.001.html.ZHOUT， DANGP， LUH L， et al. A transformer segmentation model for PET/CT images with cross-modal ［J/OL］. Journal of Electronics & Information， 2022： 1-9. （2022-12-13）. https：//kns.cnki.net/kcms/detail/11.4494.TN.20221213. 1157.001.html.（in Chinese）

[7] O J GURNEY-CHAMPION, J P KIESELMANN, K H WONG et al. A convolutional neural network for contouring metastatic lymph nodes on diffusion-weighted magnetic resonance images for assessment of radiotherapy response. Physics and Imaging in Radiation Oncology, 15, 1-7(2020).

[8] Z X TONG, Y X KANG, H YING et al. A novel framework for lymphatic invasion detection based on efficient medical images translation network, 669-673(2021).

[9] Y W LUO, J M XIN, S J LIU et al. Lymph node metastasis classification based on semi-supervised multi-view network, 675-680(2020).

[10] X Y ZHAO, P Y XIE, M M WANG et al. Deep learning-based fully automated detection and segmentation of lymph nodes on multiparametric-MRI for rectal cancer： a multicentre study. EBioMedicine, 56, 102780(2020).

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

[12] D ATKINSON, D L G HILL, P N R STOYLE et al. Automatic correction of motion artifacts in magnetic resonance images using an entropy focus criterion. IEEE Transactions on Medical Imaging, 16, 903-910(1997).

[13] [13] 周涛， 赵雅楠， 陆惠玲， 等. 医学图像实例分割： 从有候选区域向无候选区域［J］. 生物医学工程学杂志， 2022， 39（6）： 1218-1232.ZHOUT， ZHAOY N， LUH L， et al. Medical image instance segmentation： from candidate region to no candidate region［J］. Journal of Biomedical Engineering， 2022， 39（6）： 1218-1232. （in Chinese）

[14] D BOLYA, C ZHOU, F Y XIAO et al. YOLACT： real-time instance segmentation, 9156-9165(2019).

[15] [15] 周涛， 刘赟璨， 侯森宝， 等. REC-ResNet：面向COVID-19辅助诊断的特征增强模型［J］. 光学 精密工程， 2023， 31（14）： 2093-2110. doi: 10.37188/OPE.20233114.2093ZHOUT， LIUY C， HOUS B， et al. REC-ResNet： feature enhancement model for COVID-19 aided diagnosis［J］. Opt. Precision Eng.， 2023， 31（14）： 2093-2110. （in Chinese）. doi: 10.37188/OPE.20233114.2093

[16] J JIAO, H W QIN. Adaptively weighted balanced feature pyramid for object detection, 1214-1217(2023).

[17] Y J DIAO, S W LIU, X Z GAO et al. CNN based on multiscale window self-attention mechanism for radar HRRP target recognition, 281-285(2022).

[18] Z X YANG, L C ZHU, Y WU et al. Gated channel transformation for visual recognition, 11791-11800(2020).

[19] C ZHOU. Yolact++ Better Real-Time Instance Segmentation(2020).

[20] Y J GAO, X Y WANG, Q H LI. Improved cervical cell segmentation algorithm based on cascade mask RCNN, 1775-1780(2023).

[21] [21] 梁新宇， 林洗坤， 权冀川， 等. 基于深度学习的图像实例分割技术研究进展［J］. 电子学报， 2020， 48（12）： 2476-2486. doi: 10.3969/j.issn.0372-2112.2020.12.025LIANGX Y， LINX K， QUANJ CH， et al. Research on the progress of image instance segmentation based on deep learning［J］. Acta Electronica Sinica， 2020， 48（12）： 2476-2486.（in Chinese）. doi: 10.3969/j.issn.0372-2112.2020.12.025