[1] |
FITZMAURICE C, ALLEN C, BARBER R M, et al. Global,Regional,and National Cancer Incidence,Mortality,Years of Life Lost,Years Lived with Disability,and Disability-Adjusted Life-Years for 32 Cancer Groups,1990 to 2015:A Systematic Analysis for the Global Burden of Disease Study[J]. JAMA Oncology, 2017, 3(4):524-548.
doi: 10.1001/jamaoncol.2016.5688
|
[2] |
LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-Based Learning Applied to Document Recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
doi: 10.1109/5.726791
|
[3] |
RONNEBERGER O, FISCHER P, BROX T. U-Net:Convolutional Networks for Biomedical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Heidelberg:Springer, 2015:234-241.
|
[4] |
ZHOU Z, RAHMAN SIDDIQUEE M M, TAJBAKHSH N, et al. UNet++:A Nested U-Net Architecture for Medical Image Segmentation[C]//International Workshop on Deep Learning in Medical Image Analysis,Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Heidelberg:Springer, 2018:3-11.
|
[5] |
HUANG H, LIN L, TONG R, et al. UNet 3+:A Full-Scale Connected UNet for Medical Image Segmentation[C]// IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP). Piscataway:IEEE, 2020:1055-1059.
|
[6] |
ZHANG Z, LIU Q, WANG Y. Road Extraction by Deep Residual U-Net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5):749-753.
doi: 10.1109/LGRS.2018.2802944
|
[7] |
JHA D, SMEDSRUD P H, RIEGLER M A, et al. ResUnet++:An Advanced Architecture for Medical Image Segmentation[C]// IEEE International Symposium on Multimedia (ISM). Piscataway:IEEE, 2019:225-230.
|
[8] |
HU J, SHEN L, SUN G. Squeeze-and-Excitation Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2018:7132-7141.
|
[9] |
CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking Atrous Convolution for Semantic Image Segmentation (2022)[J/OL].[2022-7-6]. https://arxiv.org/abs/1706.05587.
|
[10] |
杨晓莉, 蔺素珍. 一种注意力机制的多波段图像特征级融合方法[J]. 西安电子科技大学学报, 2020, 47(1):120-127.
|
|
YANG Xiaoli, LIN Suzhen. Method for Multi-Band Image Feature-Level Fusion Based on the Attention Mechanism[J]. Journal of Xidian University, 2020, 47(1):120-127.
|
[11] |
回海生, 张雪英, 吴泽林, 等. 一种主辅路径注意力补偿的脑卒中病灶分割方法[J]. 西安电子科技大学学报, 2021, 48(4):200-208.
|
|
HUI Haisheng, ZHANG Xueying, WU Zelin, et al. Method for Stroke Lesion Segmentation Using the Primary-Auxiliary Path Attention Compensation Network[J]. Journal of Xidian University, 2021, 48(4):200-208.
|
[12] |
马思珂, 赵萌, 石凡, 等. 注意力驱动的细胞团簇细胞核分割算法[J]. 西安电子科技大学学报, 2022, 49(2):198-206.
|
|
MA Sike, ZHAO Meng, SHI Fan, et al. Attention Driven Nuclei Segmentation Method for Cell Clusters[J]. Journal of Xidian University, 2022, 49(2):198-206.
|
[13] |
FU H, CHENG J, XU Y, et al. Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation[J]. IEEE Transactions on Medical Imaging, 2018, 37(7):1597-1605.
doi: 10.1109/TMI.2018.2791488
pmid: 29969410
|
[14] |
QIN X, ZHANG Z, HUANG C, et al. U2-Net:Going Deeper with Nested U-Structure for Salient Object Detection[J]. Pattern Recognition, 2020, 106:107404.
doi: 10.1016/j.patcog.2020.107404
|
[15] |
GAO Y, ZHOU M, METAXAS D N. UTNet:A Hybrid Transformer Architecture for Medical Image Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Heidelberg:Springer, 2021:61-71.
|
[16] |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention Is All You Need[J]. Advancesin Neural Information Processing Systems, 2017,30.
|
[17] |
WU T, TANG S, ZHANG R, et al. CGnet:A Light-Weight Context Guided Network for Semantic Segmentation[J]. IEEE Transactions on Image Processing, 2020, 30:1169-1179.
doi: 10.1109/TIP.83
|
[18] |
CHEN H, QI X, YU L, et al. DCAN:Deep Contour-Aware Networks for Accurate Gland Segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2016:2487-2496.
|
[19] |
ZENG Z, XIE W, ZHANG Y, et al. RIC-Unet:An Improved Neural Network Based on UNet for Nuclei Segmentation in Histology Images[J]. IEEE Access, 2019, 7:21420-21428.
doi: 10.1109/ACCESS.2019.2896920
|
[20] |
ODA H, ROTH H R, CHIBA K, et al. BESNet:Boundary-Enhanced Segmentation of Cells in Histopathological Images[C]// International Conference on Medical Image Computing and Computer-Assisted Intervention. Heidelberg:Springer, 2018:228-236.
|
[21] |
ZHOU Y, ONDER O F, DOU Q, et al. CIA-Net:Robust Nuclei Instance Segmentation with Contour-Aware Information Aggregation[C]//International Conference on Information Processing in Medical Imaging. Heidelberg:Springer, 2019:682-693.
|
[22] |
NAYLOR P, LAE M, REYAL F, et al. Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map[J]. IEEE Transactions on Medical Imaging, 2018, 38(2):448-459.
doi: 10.1109/TMI.42
|
[23] |
CHENG F, CHEN C, WANG Y, et al. Learning Directional Feature Maps for Cardiac MRI Segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Heidelberg:Springer, 2020:108-117.
|
[24] |
HAN B, ZHANG M, GAO X, et al. Automatic Classification Method of Thyroid Pathological Images Using Multiple Magnification Factors[J]. Neurocomputing, 2021, 460:231-242.
doi: 10.1016/j.neucom.2021.07.024
|
[25] |
RUSSELL B, TORRALBA A, MURPHY K, et al. LabelMe:a Database and Web-Based Tool for Image Annotation[J]. International Journal of Computer Vision, 2008, 77(1):157-173.
doi: 10.1007/s11263-007-0090-8
|
[26] |
KUMAR N, VERMA R, SHARMA S, et al. A Dataset and a Technique for Generalized Nuclear Segmentation for Computational Pathology[J]. IEEE Transactions on Medical Imaging, 2017, 36(7):1550-1560.
doi: 10.1109/TMI.2017.2677499
pmid: 28287963
|