[1] |
赵龙, 杨长江, 林易霖, 等. 人工智能在结直肠癌病理研究中的应用进展[J]. 中华实验外科杂志, 2022, 39(3): 597-601.
|
|
ZHAO L, YANG C J, LIN Y L, et al. Application and development of artificial intelligence in pathological study of colorectal cancer[J]. Chinese Journal of Experimental Surgery, 2022, 39(3): 597-601. (in Chinese)
|
[2] |
王嫣然, 陈清亮, 吴俊君. 面向复杂环境的图像语义分割方法综述[J]. 计算机科学, 2019, 46(9): 36-46.
|
|
WANG Y R, CHEN Q L, WU J J. Research on image semantic segmentation for complex environments[J]. Computer Science, 2019, 46(9): 36-46. (in Chinese)
DOI
|
[3] |
陈超, 齐峰. 卷积神经网络的发展及其在计算机视觉领域中的应用综述[J]. 计算机科学, 2019, 46(3): 63-73.
DOI
|
|
CHEN C, QI F. Review on development of convolutional neural network and its application in computer vision[J]. Computer Science, 2019, 46(3): 63-73. (in Chinese)
DOI
|
[4] |
LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]// 2015 IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2015: 3431-3440.
|
[5] |
RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[M]// Lecture Notes in Computer Science. Cham: Springer International Publishing, 2015: 234-241.
|
[6] |
李翠云, 白静, 郑凉. 融合边缘增强注意力机制和U-Net网络的医学图像分割[J]. 图学学报, 2022, 43(2): 273-278.
|
|
LI C Y, BAI J, ZHENG L. A U-Net based contour enhanced attention for medical image segmentation[J]. Journal of Graphics, 2022, 43(2): 273-278. (in Chinese)
|
[7] |
陈铭, 梅雪, 朱文俊, 等. 一种新型Mobile Unet网络的肺结节图像分割方法[J]. 南京工业大学学报: 自然科学版, 2022(1): 76-81, 91.
|
|
CHEN M, MEI X, ZHU W J, et al. A novel pulmonarynodule segmentation method using Mobile Unet network[J]. Journal of Nanjing Tech University: Natural Science Edition, 2022(1): 76-81, 91. (in Chinese)
|
[8] |
HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]// 2019 IEEE/CVF International Conference on Computer Vision. New York: IEEE Press, 2020: 1314-1324.
|
[9] |
ZHOU Z W, SIDDIQUEE M M R, TAJBAKHSH N, et al. UNet: redesigning skip connections to exploit multiscale features in image segmentation[J]. IEEE Transactions on Medical Imaging, 2020, 39(6): 1856-1867.
DOI
URL
|
[10] |
FAN D P, JI G P, ZHOU T, et al. PraNet: parallel reverse attention network for polyp segmentation[M]// Medical Image Computing and Computer Assisted Intervention. Cham: Springer International Publishing, 2020: 263-273.
|
[11] |
JHA D, RIEGLER M A, JOHANSEN D, et al. DoubleU-net: a deep convolutional neural network for medical image segmentation[C]// 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems. New York: IEEE Press, 2020: 558-564.
|
[12] |
CHEN L C, ZHU Y K, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]//Computer Vision - ECCV 2018. Cham: Springer International Publishing, 2018: 833-851.
|
[13] |
XU Q, MA Z C, HE N, et al. DCSAU-net: a deeper and more compact split-attention U-net for medical image segmentation[EB/OL]. [2022-09-24]. https://arxiv.org/abs/2202.00972.
|
[14] |
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. New York: IEEE Press, 2020: 225-2255.
|
[15] |
ZHANG Z X, LIU Q J, WANG Y H. Road extraction by deep residual U-net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749-753.
DOI
URL
|
[16] |
HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York: IEEE Press, 2018: 7132-7141.
|
[17] |
YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[EB/OL]. [2022-04-18]. https://arxiv.org/abs/1511.07122.
|
[18] |
曲长波, 姜思瑶, 吴德阳. 空洞卷积的多尺度语义分割网络[J]. 计算机工程与应用, 2019, 55(24): 91-95.
DOI
|
|
QU C B, JIANG S Y, WU D Y. Multiscale semantic segmentation network based on cavity convolution[J]. Computer Engineering and Applications, 2019, 55(24): 91-95. (in Chinese)
DOI
|
[19] |
王军, 冯孙铖, 程勇. 深度学习的轻量化神经网络结构研究综述[J]. 计算机工程, 2021, 47(8): 1-13.
DOI
|
|
WANG J, FENG S C, CHENG Y. Survey of research on lightweight neural network structures for deep learning[J]. Computer Engineering, 2021, 47(8): 1-13. (in Chinese)
DOI
|
[20] |
MA N N, ZHANG X Y, ZHENG H T, et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]// Computer Vision - ECCV 2018. Cham: Springer International Publishing, 2018: 122-138.
|
[21] |
冯兴杰, 张天泽. 基于分组卷积进行特征融合的全景分割算法[J]. 计算机应用, 2021, 41(7): 2054-2061.
DOI
|
|
FENG X J, ZHANG T Z. Panoptic segmentation algorithm based on grouped convolution for feature fusion[J]. Journal of Computer Applications, 2021, 41(7): 2054-2061. (in Chinese)
DOI
|
[22] |
IBTEHAZ N, RAHMAN M S. MultiResUNet: Rethinking the U-Net architecture for multimodal biomedical image segmentation[J]. Neural Networks, 2020, 121: 74-87.
DOI
URL
|
[23] |
ZHANG H, ZU K K, LU J, et al. EPSANet: an efficient pyramid split attention block on convolutional neural network[EB/OL]. [2022-07-22]. https://arxiv.org/abs/2105.14447.
|
[24] |
BERNAL J, TAJKBAKSH N, SANCHEZ F J, et al. Comparative validation of polyp detection methods in video colonoscopy: results from the MICCAI 2015 endoscopic vision challenge[J]. IEEE Transactions on Medical Imaging, 2017, 36(6): 1231-1249.
DOI
PMID
|
[25] |
OKTAY O, SCHLEMPER J, FOLGOC L L, et al. Attention U-net: learning where to look for the pancreas[EB/OL]. [2022-05-20]. https://arxiv.org/abs/1804.03999.
|