计算机技术、信息工程 |
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基于Cycle-GAN和改进DPN网络的乳腺癌病理图像分类 |
张雪芹(),李天任 |
华东理工大学 信息科学与工程学院,上海 200000 |
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Breast cancer pathological image classification based on Cycle-GAN and improved DPN network |
Xue-qin ZHANG(),Tian-ren LI |
School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200000, China |
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CHEN W, ZHENG R, BAADE P, et al Cancer Statistics in China, 2015[J]. CA: A Cancer Journal for Clinicians, 2016, 66 (2): 115- 132
doi: 10.3322/caac.21338
|
2 |
KOWAL M, FILIPCZUK P, OBUCHOWICZ A, et al Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images[J]. Computers in Biology and Medicine, 2013, 43 (10): 1563- 1572
doi: 10.1016/j.compbiomed.2013.08.003
|
3 |
ISABEL G, POLONIA A, SARMIENTO A, et al Automatic classification of tissue malignancy for breast carcinoma diagnosis[J]. Computers in Biology and Medicine, 2018, 96: 41- 51
doi: 10.1016/j.compbiomed.2018.03.003
|
4 |
ABDULLAH-AL N, YINAN K Histopathological breast-image classification using concatenated R-G-B histogram information[J]. Annals of Data Science, 2019, 6 (3): 513- 529
doi: 10.1007/s40745-018-0162-3
|
5 |
WANG P, HU X, LI Y, et al Automatic cell nuclei segmentation and classification of breast cancer histopathology images[J]. Signal Processing, 2016, 122: 1- 13
doi: 10.1016/j.sigpro.2015.11.011
|
6 |
SPANHOL F, OLIVEIRA L, PETITJEAN C, et al A dataset for breast cancer histopathological image classification[J]. IEEE Transactions on Biomedical Engineering, 2016, 63 (7): 1455- 1462
doi: 10.1109/TBME.2015.2496264
|
7 |
VETA M, PLUIM J, VAN D, et al Breast cancer histopathology image analysis: a rcview[J]. IEEE Transactions on Biomedical Engineering, 2014, 61 (5): 1400- 1411
doi: 10.1109/TBME.2014.2303852
|
8 |
SHEN D, WU G, SUK H Deep learning in medical image analysis[J]. Annual Review of Biomedical Engineering, 2017, 19 (1): 221- 248
doi: 10.1146/annurev-bioeng-071516-044442
|
9 |
SPANHOL F, OLIVEIRA L, PETITJEAN C, et al. Breast cancer histopathological image classification using convolutional neural networks [C]// Proceedings of 2016 International Joint Conference on Neural Networks. Vancouver: IEEE, 2016: 2560-2567.
|
10 |
BAYRAMOGLU N, KANNALA J, JANNE H. Deep learning for magnification independent breast cancer histopathology image classification [C]// Proceedings of International Conference on Pattern Recognition. Cancun: IEEE, 2016: 2441-2446.
|
11 |
何雪英, 韩忠义, 魏本征 基于深度学习的乳腺癌病理图像自动分类[J]. 计算机工程与应用, 2018, 54 (12): 121- 125 HE Xue-ying, HAN Zhong-yi, WEI Ben-zheng Breast cancer histopathological image auto-classification using deep learning[J]. Computer Engineering and Applications, 2018, 54 (12): 121- 125
doi: 10.3778/j.issn.1002-8331.1701-0392
|
12 |
明涛, 王丹, 郭继昌, 等 基于多尺度通道重校准的乳腺癌病理图像分类[J]. 浙江大学学报:工学版, 2020, 54 (7): 1289- 1297 MING Tao, WANG Dan, GUO Ji-chang, et al Breast cancer histopathological image classification using multi-scale channel squeeze-and-excitation model[J]. Journal of Zhejiang University: Engineering Science, 2020, 54 (7): 1289- 1297
|
13 |
邹文凯, 陆慧娟, 叶敏超, 等 基于卷积神经网络的乳腺癌组织病理图像分类[J]. 计算机工程与设计, 2020, 41 (6): 1749- 1754 ZOU Wen-kai, LU Hui-juan, YE Min-chao, et al Breast cancer histopathological image classification using convolutional neural network[J]. Computer Engineering and Design, 2020, 41 (6): 1749- 1754
|
14 |
NAHID A, MEHRABI M, KONG Y. Frequency-domain information along with LSTM and GRU methods for histopathological breast-image classification [C]//International Symposium on Signal Processing and Information Technology. Bilbao: IEEE, 2017: 410-415.
|
15 |
GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks [C]// Advances in Neural Information Processing Systems. Montreal: MIT Press, 2014: 2672-2680.
|
16 |
ZHU J, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks [C]// International Conference On Computer Vision. Venice: IEEE, 2017: 2242-2251.
|
17 |
HUANG G, LIU Z, LAURENS V, et al. Densely connected convolutional networks [C]// Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 2261-2269.
|
18 |
CHEN Y, LI J, XIAO H, et al. Dual path networks [C]// Conference on Neural Information Processing Systems. Long Beach: IEEE, 2017: 1-9.
|
19 |
JIE H, LI S, GANG S, et al Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 42 (99): 2011- 2023
|
20 |
SHORTEN C, KHOSHGOFTAAR T A survey on image data augmentation for deep learning[J]. Journal of Big Data, 2019, 6 (1): 1- 48
doi: 10.1186/s40537-018-0162-3
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