Citation: | LIU Chang, ZHENG Yuchao, XIE Wenqian, LI Chen, LI Xiaohan. Image Recognition Method of Cervical Adenocarcinoma in Situ Based on Deep Learning[J]. Medical Journal of Peking Union Medical College Hospital, 2023, 14(1): 159-167. DOI: 10.12290/xhyxzz.2022-0109 |
[1] |
Cao W, Chen HD, Yu YW, et al. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020[J]. Chin Med J, 2021, 134: 783-791. DOI: 10.1097/CM9.0000000000001474
|
[2] |
Baalbergen A, Helmerhorst TJ. Adenocarcinoma in situ of the uterine cervix: a systematic review[J]. Int J Gynecol Cancer, 2014, 24: 1543-1548. DOI: 10.1097/IGC.0000000000000260
|
[3] |
Cleveland AA, Gargano JW, Park IU, et al. Cervical adenocarcinoma in situ: human papillomavirus types and incidence trends in five states, 2008—2015[J]. Int J Cancer, 2020, 146: 810-818. DOI: 10.1002/ijc.32340
|
[4] |
刘从容. 宫颈腺上皮病变病理学相关问题及其研究进展[J]. 中华妇幼临床医学杂志, 2016, 12: 2-6. DOI: 10.3877/cma.j.issn.1673-5250.2016.01.001
Liu CR. Pathologic problems and research progress of cervical adenoepithelial lesions[J]. Zhonghua Fuyou Linchuang Yixue Zazhi, 2016, 12: 2-6. DOI: 10.3877/cma.j.issn.1673-5250.2016.01.001
|
[5] |
Liu J, Li L, Wang L. Acetowhite region segmentation in uterine cervix images using a registered ratio image[J]. Comput Biol Med, 2018, 93: 47-55. DOI: 10.1016/j.compbiomed.2017.12.009
|
[6] |
Xin TL, Chen L, Md Mamunur R, et al. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches[J]. Artifi Intell Rev, 2022, 55: 4809-4878. DOI: 10.1007/s10462-021-10121-0
|
[7] |
Song D, Kim E, Huang X, et al. Multimodal entity coreference for cervical dysplasia diagnosis[J]. IEEE Trans Med Imaging, 2015, 34: 229-245. DOI: 10.1109/TMI.2014.2352311
|
[8] |
Asiedu MN, Simhal A, Chaudhary U, et al. Development of algorithms for automated detection of cervical pre-cancers with a low-cost, point-of-care, Pocket Colposcope[J]. IEEE Trans Biomed Eng, 2019, 66: 2306-2318. DOI: 10.1109/TBME.2018.2887208
|
[9] |
Li C, Chen H, Li XY, et al. A review for cervical histopathology image analysis using machine vision approaches[J]. Artifi Intell Rev, 2020, 53: 4821-4862. DOI: 10.1007/s10462-020-09808-7
|
[10] |
庄福振, 罗平, 何清, 等. 迁移学习研究进展[J]. 软件学报, 2015, 26: 26-39. https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201501003.htm
Zhuang FZ, Luo P, He Q, et al. Research progress of transfer learning[J]. Ruanjian Xuebao, 2015, 26: 26-39. https://www.cnki.com.cn/Article/CJFDTOTAL-RJXB201501003.htm
|
[11] |
Jun L, Guang YL, Xiang RT, et al. Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction[J]. Comput Biol Med, 2021, 134: 104504. DOI: 10.1016/j.compbiomed.2021.104504
|
[12] |
Niu S, Liu M, Liu Y, et al. Distant Domain Transfer Learning for Medical Imaging[J]. IEEE J Biomed Health Inform, 2021, 25: 3784-3793. DOI: 10.1109/JBHI.2021.3051470
|
[13] |
颜悦, 陈丽萌, 李锦涛, 等. 基于深度学习和组织病理图像的癌症分类研究进展[J]. 协和医学杂志, 2021, 12: 742-748. DOI: 10.12290/xhyxzz.2021-0452
Yan Y, Chen LM, Li JT, et al. Progress in cancer classification based on deep learning and histopathological images[J]. Xiehe Yixue Zazhi, 201, 12: 742-748. DOI: 10.12290/xhyxzz.2021-0452
|
[14] |
杨志明, 李亚伟, 杨冰, 等. 融合宫颈细胞领域特征的多流卷积神经网络分类算法[J]. 计算机辅助设计与图形学学报, 2019, 31: 531-540. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201904003.htm
Yang ZM, Li YW, Yang B, et al. Multi-flow convolutional neural network classification algorithm based on domain features of cervical cells[J]. Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao, 2019, 31: 531-540. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201904003.htm
|
[15] |
Qiao ZH, Herve D, Nicolas D, et al. 3-D consistent and robust segmentation of cardiac images by deep learning with spatial propagation[J]. IEEE Trans Med Imaging, 2018, 37: 2137-2148. DOI: 10.1109/TMI.2018.2820742
|
[16] |
Yang H, Sun J, Li H, et al. Neural multi-atlas label fusion: Application to cardiac MR images[J]. Med image Anal, 2018, 49: 60-75. DOI: 10.1016/j.media.2018.07.009
|
[17] |
Jamaludin A, Kadir T, Zisserman A. SpineNet: automated classification and evidence visualization in spinal MRIs[J]. Med image Anal, 2017, 41: 63-73. DOI: 10.1016/j.media.2017.07.002
|
[18] |
Sato M, Horie K, Hara A, et al. Application of deep learning to the classification of images from colposcopy[J]. Oncol lett, 2018, 15: 3518-3523.
|
[19] |
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Comput Sci, 2014. https://doi.org/10.48550/arXiv.1409.1556.
|
[20] |
Russakovsky O, Deng J, Su H, et al. Imagenet large scale visual recognition challenge[J]. Int J Comput Vision, 2015, 115: 211-252.
|
[21] |
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015: 1-9.
|
[22] |
Chollet F. Xception: Deep learning with depthwise separable convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251-1258.
|
[23] |
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Confer-ence on Computer Vision and Pattern Recognition, 2016: 770-778.
|
[24] |
Huang G, Liu Z, Van DML, et al. Densely connected convolutional networks[C]. Proceedings of the IEEE Confer-ence on Computer VIsion and Pattern Recognition, 2017: 4700-4708.
|
[25] |
Al-Haija QA, Adebanjo A. Breast Cancer Diagnosis in Histopathological Images Using ResNet-50 Convolutional Neural Network[C]. 2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS). IEEE, 2020.
|
[1] | Rare Diseases Society of Chinese Research Hospital Association, National Rare Diseases Committee, Beijing Rare Disease Diagnosis, Treatment and Protection Society, Gitelman Syndrome Consensus Working Group. Expert Consensus for the Diagnosis and Treatment of Gitelman Syndrome in China (2021)[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 902-912. DOI: 10.12290/xhyxzz.2021-0555 |
[2] | YAN Rui, CHEN Limeng, LI Jintao, REN Fei. Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 742-748. DOI: 10.12290/xhyxzz.2021-0452 |
[3] | LI Xirong. Multi-modal Deep Learning and Its Applications in Ophthalmic Artificial Intelligence[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(5): 602-607. DOI: 10.12290/xhyxzz.2021-0500 |
[4] | Qi JIN, Qin LUO, Zhi-hui ZHAO, Zhi-hong LIU. MicroRNA-21 in the Pathogenesis of Pulmonary Hypertension[J]. Medical Journal of Peking Union Medical College Hospital, 2020, 11(4): 430-438. DOI: 10.3969/j.issn.1674-9081.2020.04.013 |
[5] | Jia-qi ZHANG, Lei LIU, Gui-ge WANG, Wen-liang BAI, Shan-qing LI. Clinical Pathological Features and Prognosis of Non-small Cell Lung Cancer with Skip N2 Lymph Node Metastasis[J]. Medical Journal of Peking Union Medical College Hospital, 2019, 10(3): 272-277. DOI: 10.3969/j.issn.1674-9081.2019.03.015 |
[6] | Hang-ning ZHOU, Feng-ying XIE, Zhi-guo JIANG, Jie LIU, Hong-zhong JIN, Ru-song MENG, Yong CUI. Classification of Skin Images Based on Deep Learning[J]. Medical Journal of Peking Union Medical College Hospital, 2018, 9(1): 15-18. DOI: 10.3969/j.issn.1674-9081.2018.01.004 |
[9] | Zhi-lan MENG, Liang GAO, Jian-gang GU, Yu-feng LUO, Tao-ling ZHONG, Chen-yan ZHU. Application of Automatic DNA Image Cytometry in Diagnosis of Pleural Effusion[J]. Medical Journal of Peking Union Medical College Hospital, 2012, 3(1): 36-40. DOI: 10.3969/j.issn.1674-9081.2012.01.009 |
[10] | Xin-yu REN, Yu-feng YIN, Jie GAO, Sha-fei WU, Ke WANG, Wen-ze WANG, Xuan ZENG, Zhi-yong LIANG. Detection of HER2/neu Gene in Pancreatic and Gastric Adenocarcinoma among Chinese Patients[J]. Medical Journal of Peking Union Medical College Hospital, 2012, 3(1): 21-25. DOI: 10.3969/j.issn.1674-9081.2012.01.006 |
1. |
安阳,刘美玲. 缓和医疗护理模式在晚期癌症患者中的应用. 中西医结合护理(中英文). 2024(06): 17-20 .
![]() | |
2. |
许亚文,王英,谌永毅,肖亚洲,郭俊晨,刘阳,赵海伦. 终末期肿瘤患者主要照顾者对数字化健康干预认知及需求的质性研究. 中国实用护理杂志. 2024(31): 2448-2454 .
![]() |