目标检测在肺癌病理诊断中的应用

doi:10.3969/j.issn.1000-1565.2023.05.014

河北大学学报(自然科学版) ›› 2023, Vol. 43 ›› Issue (5): 546-552.DOI: 10.3969/j.issn.1000-1565.2023.05.014

目标检测在肺癌病理诊断中的应用

武建国¹,杨晓茹²,王盼³,吴俊芳⁴,李瑞凯^1,5

收稿日期:2022-06-23 出版日期:2023-09-25 发布日期:2023-10-25
通讯作者: 李瑞凯(1991—)
作者简介:武建国(1985—),男,河北保定人,河北大学附属医院高级工程师,主要从事人工智能在医学影像诊断中的研究.
E-mail:wujianguo1111@126.com
基金资助:
河北大学附属医院基金资助项目(2019Q003)

Application of object detection algorithm in pathology diagnosis of lung cancer

WU Jianguo¹,YANG Xiaoru²,WANG Pan³,WU Junfang⁴,LI Ruikai^1,5

1. Information Center, Affiliated Hospital of Hebei University, Baoding 071000, China; 2. Baoding Productivity Promotion Center, Baoding 071000, China; 3. Department of Pathology, Affiliated Hospital of Hebei University, Baoding 071000, China; 4. Baoding Sports School, Baoding 071000, China; 5. College of Quality and Technical Supervision, Hebei University, Baoding 071002, China

Received:2022-06-23 Online:2023-09-25 Published:2023-10-25

摘要/Abstract

摘要： 基于深度学习的目标检测已经在交通、军事等多个领域得到了广泛研究,并取得了显著成果.为进一步研究其在医学图像诊断中的可用性,提出将目标检测算法的2类经典模型SSD和Faster RCNN应用于肺癌病理图像的病灶检测.实验对比发现,Faster RCNN平均处理1张图像所消耗的时间大概是SSD时长的4.6倍,但其识别准确度、精度均比SSD更高,模型性能更优.研究结果表明,目标检测算法能够实现肺癌病理图像的智能诊断,提高肺癌诊断率.

关键词: 深度学习, 目标检测, 肺癌, 病理图像, 智能诊断

Abstract: Object detection based on deep learning has been widely studied in many fields such as transportation and military, and has achieved remarkable results. In order to further study its usability in medical image diagnosis, two classical models of object detection algorithms, SSD and Faster RCNN, are proposed to appliy to the lesion detection of lung cancer pathological images. The experimental comparison shows that the average processing time of Faster RCNN is about 4.6 times longer than that of SSD, but the recognition accuracy and precision are higher than that of SSD, and the model performance of Faster RCNN is much better. The research results show that the object detection algorithm can realize the intelligent diagnosis of lung cancer pathological images and improve the diagnosis rate of lung cancer.

Key words: deep learning, object detection, lung cancer, pathological image, intelligent diagnosis

中图分类号:

TP399

武建国,杨晓茹,王盼,吴俊芳,李瑞凯. 目标检测在肺癌病理诊断中的应用[J]. 河北大学学报(自然科学版), 2023, 43(5): 546-552.

WU Jianguo,YANG Xiaoru,WANG Pan,WU Junfang,LI Ruikai. Application of object detection algorithm in pathology diagnosis of lung cancer[J]. Journal of Hebei University(Natural Science Edition), 2023, 43(5): 546-552.

参考文献

[1] SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics[J]. CA Cancer J Clin, 2020, 70(1): 7-30. DOI: 10.3322/caac.21590.
[2] HE Y F, WEI J, CHE S L, et al. Computer-aided pathological annotation framework: a deep learning-based diagnostic algorithm of lung cancer[C] //2019 International Conference on Information Technology and Computer Application(ITCA), 2019, Guangzhou, China, IEEE, 2020: 110-113. DOI: 10.1109/ITCA49981.2019.00032.
[3] ESTEVA A, KUPREL B, NOVOA R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118. DOI: 10.1038/nature21056.
[4] SIRINUKUNWATTANA K, RAZA S E A, TSANG Y W, et al. Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images[J]. IEEE T Med Imaging, 2016, 35(5): 1196-1206. DOI: 10.1109/TMI.2016.2525803.
[5] SHARMA H, ZERBE N, KLEMPERT I, et al. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology[J]. Comput Med Imaging Graph, 2017, 61: 2-13. DOI: 10.1016/j.compmedimag.2017.06.001.
[6] WAHAB N, KHAN A, LEE Y S. Two-phase deep convolutional neural network for reducing class skewness in histopathological images based breast cancer detection[J]. Comput Biol Med, 2017, 85: 86-97. DOI: 10.1016/j.compbiomed.2017.04.012.
[7] WAN T, CAO J J, CHEN J H, et al. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features[J]. Neurocomputing, 2017, 229: 34-44. DOI: 10.1016/j.neucom.2016.05.084.
[8] XU X W, HOU R P, ZHAO W Y, et al. A weak supervision-based framework for automatic lung cancer classification on whole slide image[C] //2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society(EMBC), 2020, Montreal, QC, Canada, IEEE, 2020: 1372-1375. DOI: 10.1109/EMBC44109.2020.9176620.
[9] DENIZ N A, SERKAN O. Lung cancer classification and detection using convolutional neural networks[C] //Proceedings of the 6th International Conference on Engineering & MIS 2020, 2020, Almaty, Kazakhstan, New York: ACM, 2020: 1-8. DOI: 10.1145/3410352.3410822.
[10] ABUBAKIR AHMED H, ABDULLA MAHMOOD S. A deep learning technique for lung nodule classification based on false positive reduction[J]. J Zankoy Sulaimani A, 2019, 21(1): 107-116. DOI: 10.17656/jzs.10749.
[11] PANG S C, MENG F, WANG X, et al. VGG16-T: a novel deep convolutional neural network with boosting to identify pathological type of lung cancer in early stage by CT images[J]. Int J Comput Int Sys, 2020, 13(1): 771. DOI: 10.2991/ijcis.d.200608.001.
[12] JAKIMOVSKI G, DAVCEV D. Using double convolution neural network for lung cancer stage detection[J]. Appl Sci, 2019, 9(3): 427. DOI: 10.3390/app9030427.
[13] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C] //2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Las Vegas, NV, USA, IEEE, 2016: 779-788. DOI: 10.1109/CVPR.2016.91.
[14] LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[M] //Computer Vision – ECCV 2016. Cham: Springer International Publishing, 2016: 21-37. DOI: 10.1007/978-3-319-46448-0_2.
[15] GIRSHICK R. Fast R-CNN[C] //2015 IEEE International Conference on Computer Vision(ICCV), 2015, Santiago, Chile, IEEE, 2016: 1440-1448. DOI: 10.1109/ICCV.2015.169.
[16] REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C] //IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE, 2016: 1137-1149. DOI: 10.1109/TPAMI.2016.2577031.
[17] LAW H, DENG J. CornerNet: detecting objects as paired keypoints[J]. Int J Comput Vision, 2020, 128(3): 642-656. DOI: 10.1007/s11263-019-01204-1.
[18] HE K M, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C] //2017 IEEE International Conference on Computer Vision(ICCV), Venice, Italy. IEEE, 2017: 2980-2988. DOI: 10.1109/ICCV.2017.322.
[19] EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The pascal visual object classes(VOC)challenge[J]. Int J Comput Vision, 2010, 88(2): 303-338. DOI: 10.1007/s11263-009-0275-4.
[20] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. [2022-02-26]. https://arxiv.org/abs/1409.1556.
[21] JANOWCZYK A, MADABHUSHI A. Deep learning for digital pathology image analysis: a comprehensive tutorial with selected use cases[J]. J Pathol Inform, 2016, 7(1): 29. DOI: 10.4103/2153-3539.186902. (

目标检测在肺癌病理诊断中的应用

Application of object detection algorithm in pathology diagnosis of lung cancer

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