1 |
ABRÀMOFF M D, GARVIN M K, SONKA M. Retinal imaging and image analysis. IEEE Reviews in Biomedical Engineering, 2010, 3, 169- 208.
doi: 10.1109/RBME.2010.2084567
|
2 |
ROUHI R, JAFARI M, KASAEI S, et al. Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Systems with Applications, 2015, 42(3): 990- 1002.
doi: 10.1016/j.eswa.2014.09.020
|
3 |
AQUINO A, GEGÚNDEZ-ARIAS M E, MARÍN D. Detecting the optic disc boundary in digital fundus images using morphological, edge detection, and feature extraction techniques. IEEE Transactions on Medical Imaging, 2010, 29(11): 1860- 1869.
doi: 10.1109/TMI.2010.2053042
|
4 |
LEE Y, HARA T, FUJITA H, et al. Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique. IEEE Transactions on Medical Imaging, 2001, 20(7): 595- 604.
doi: 10.1109/42.932744
|
5 |
|
6 |
LECUN Y, BENGIO Y, HINTON G. Deep learning. Nature, 2015, 521(7553): 436- 444.
doi: 10.1038/nature14539
|
7 |
PHAM D L, XU C, PRINCE J L. Current methods in medical image segmentation. Annual Review of Biomedical Engineering, 2000, 2, 315- 337.
doi: 10.1146/annurev.bioeng.2.1.315
|
8 |
ZHENG Y H, JEON B, XU D H, et al. Image segmentation by generalized hierarchical fuzzy C-means algorithm. Journal of Intelligent & Fuzzy Systems, 2015, 28(2): 961- 973.
|
9 |
CHEN H, QI X J, YU L Q, et al. DCAN: deep contour-aware networks for object instance segmentation from histology images. Medical Image Analysis, 2017, 36, 135- 146.
doi: 10.1016/j.media.2016.11.004
|
10 |
RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2015: 234-241.
|
11 |
WANG C, ZHAO Z Y, REN Q Q, et al. Dense U-Net based on patch-based learning for retinal vessel segmentation. Entropy, 2019, 21(2): 168.
doi: 10.3390/e21020168
|
12 |
XIAO X, LIAN S, LUO Z M, et al. Weighted Res-UNet for high-quality retina vessel segmentation[C]//Proceedings of the 9th International Conference on Information Technology in Medicine and Education. Washington D. C., USA: IEEE Press, 2018: 327-331.
|
13 |
LIN B S, MICHAEL K, KALRA S, et al. Skin lesion segmentation: U-Nets versus clustering[C]//Proceedings of IEEE Symposium Series on Computational Intelligence. Washington D. C., USA: IEEE Press, 2018: 1-7.
|
14 |
SIRINUKUNWATTANA K, PLUIM J P W, CHEN H, et al. Gland segmentation in colon histology images: the GLAS challenge contest. Medical Image Analysis, 2017, 35, 489- 502.
doi: 10.1016/j.media.2016.08.008
|
15 |
ÇIÇEK Ö, ABDULKADIR A, LIENKAMP S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention. Berlin, Germany: Springer, 2016: 424-432.
|
16 |
SETIO A A A, TRAVERSO A, DE BEL T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. Medical Image Analysis, 2017, 42, 1- 13.
doi: 10.1016/j.media.2017.06.015
|
17 |
YU L Q, YANG X, CHEN H, et al. Volumetric ConvNets with mixed residual connections for automated prostate segmentation from 3D MR images[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 66-72.
|
18 |
IGLOVIKOV V, SHVETS A. TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation[EB/OL]. [2022-09-11]. https://arxiv.org/abs/1801.05746.
|
19 |
|
20 |
KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks. Communications of the ACM, 2017, 60(6): 84- 90.
doi: 10.1145/3065386
|
21 |
ZHOU Z W, MAHFUZUR R S, TAJBAKHSH N, et al. U-Net++: a nested U-Net architecture for medical image segmentation. Berlin, Germany: Springer International Publishing, 2018.
|
22 |
|
23 |
ALOM M Z, HASAN M, YAKOPCIC C, et al. Recurrent residual convolutional neural network based on U-Net(R2U-Net) for medical image segmentation[EB/OL]. [2022-09-11]. https://arxiv.org/abs/1802.06955.
|
24 |
YANG X, LI Z Q, GUO Y Q, et al. DCU-Net: a deformable convolutional neural network based on cascade U-Net for retinal vessel segmentation. Multimedia Tools and Applications, 2022, 81(11): 15593- 15607.
doi: 10.1007/s11042-022-12418-w
|
25 |
朱辉, 秦品乐. 基于多尺度特征结构的U-Net肺结节检测算法. 计算机工程, 2019, 45(4): 254- 261.
doi: 10.19678/j.issn.1000-3428.0051769
|
|
ZHU H, QIN P L. U-Net pulmonary nodule detection algorithm based on multi-scale feature structure. Computer Engineering, 2019, 45(4): 254- 261.
doi: 10.19678/j.issn.1000-3428.0051769
|
26 |
SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]//Proceedings of the 31st AAAI Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 4278-4284.
|
27 |
SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2016: 2818-2826.
|
28 |
袁单飞, 陈慈发, 董方敏. 基于多尺度分割的图像识别残差网络研究. 计算机工程, 2022, 48(5): 258-262, 271.
doi: 10.19678/j.issn.1000-3428.0061392
|
|
YUAN D F, CHEN C F, DONG F M. Research on residual network of image recognition based on multiscale split. Computer Engineering, 2022, 48(5): 258-262, 271.
doi: 10.19678/j.issn.1000-3428.0061392
|
29 |
SANDLER M, HOWARD A, ZHU M L, et al. MobileNetv2: inverted residuals and linear bottlenecks[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2018: 4510-4520.
|
30 |
CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: IEEE Press, 2017: 1800-1807.
|
31 |
曹渝昆, 桂丽嫒. 基于深度可分离卷积的轻量级时间卷积网络设计. 计算机工程, 2020, 46(9): 95-100, 109.
URL
|
|
CAO Y K, GUI L A. Design of lightweight temporal convolutional network based on depthwise separable convolution. Computer Engineering, 2020, 46(9): 95-100, 109.
URL
|
32 |
周东明, 张灿龙, 唐艳平, 等. 联合语义分割与注意力机制的行人再识别模型. 计算机工程, 2022, 48(2): 201- 206.
URL
|
|
ZHOU D M, ZHANG C L, TANG Y P, et al. Pedestrian re-identification model combining semantic segmentation and attention mechanism. Computer Engineering, 2022, 48(2): 201- 206.
URL
|
33 |
LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis. Medical Image Analysis, 2017, 42, 60- 88.
doi: 10.1016/j.media.2017.07.005
|
34 |
CODELLA N C F, GUTMAN D, CELEBI M E, et al. Skin lesion analysis toward melanoma detection[C]//Proceedings of the 15th International Symposium on Biomedical Imaging. Washington, USA. IEEE Press, 2018: 168-172.
|
35 |
BERNAL J, SÁNCHEZ F J, FERNÁNDEZ-ESPARRACH G, et al. WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Computerized Medical Imaging and Graphics, 2015, 43, 99- 111.
doi: 10.1016/j.compmedimag.2015.02.007
|
36 |
ARGANDA-CARRERAS I, TURAGA S C, BERGER D R, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Frontiers in Neuroanatomy, 2015, 9, 142.
|
37 |
CARDONA A, SAALFELD S, PREIBISCH S, et al. An integrated micro- and macro-architectural analysis of the Drosophila brain by computer-assisted serial section electron microscopy. PLoS Biology, 2010, 8(10): e1000502.
doi: 10.1371/journal.pbio.1000502
|
38 |
VAN ROSSUM G. Python programming language[C]//Proceedings of USENIX Annual Technical Conference. Washington D. C., USA: IEEE Press, 2007: 1-36.
|
39 |
MCGUINNESS K, O'CONNOR N E. A comparative evaluation of interactive segmentation algorithms. Pattern Recognition, 2010, 43(2): 434- 444.
doi: 10.1016/j.patcog.2009.03.008
|
40 |
楼鑫杰, 李小薪, 刘志勇. 基于反馈机制的图像超分辨率重建算法. 计算机工程, 2022, 48(2): 261- 267.
URL
|
|
LOU X J, LI X X, LIU Z Y. Super-resolution image reconstruction algorithm based on feedback mechanism. Computer Engineering, 2022, 48(2): 261- 267.
URL
|
41 |
KOHAVI R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C]//Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Francisco, USA: Morgan Kaufmann Publishers Inc., 1995: 1137-1145.
|