Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

YAN Rui; CHEN Limeng; LI Jintao; REN Fei

doi:10.12290/xhyxzz.2021-0452

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Nov 2021

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JOURNAL OF MECHANICAL ENGINEERING > 2021 > 12(5): 742-748.

YAN Rui, CHEN Limeng, LI Jintao, REN Fei. Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 12(5): 742-748. doi: 10.12290/xhyxzz.2021-0452

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YAN Rui, CHEN Limeng, LI Jintao, REN Fei. Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 12(5): 742-748. doi: 10.12290/xhyxzz.2021-0452

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Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

doi: 10.12290/xhyxzz.2021-0452

YAN Rui^{1, 2},
CHEN Limeng³,
LI Jintao¹,
REN Fei^{1
,
,}

Funds:

National Natural Science Foundation of China 82072939

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Corresponding author: REN Fei Tel: 86-10-62600343, E-mail: renfei@ict.ac.cn
Received Date: 07 Jun 2021
Accepted Date: 29 Jul 2021

Available Online: 26 Nov 2021

Publish Date: 16 Sep 2021
Issue Publish Date: 30 Sep 2021

Abstract

Abstract

Accurate classification of cancer is directly related to the choice of treatment options and prognosis. Pathological diagnosis is the gold standard for cancer diagnosis. The digitalization of pathological images and breakthroughs in deep learning have made computer-aided diagnosis and prediction about prognosis possible. In this paper, we first briefly describe four deep learning methods commonly used in this field, and then review the latest research progress in cancer classification based on deep learning and histopathological images. Finally, the general problems in this field are summarized, and the possible development direction in the future is suggested.
- pathological images,
- deep learning,
- cancer classification,
- cancer grading,
- computer-aided diagnosis

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References

[1]	Siegel RL, Miller KD, Dvm AJ. Cancer statistics, 2019[J]. CA Cancer J Clin, 2019, 69: 7-34. doi: 10.3322/caac.21551
[2]	卞修武, 平轶芳. 我国病理学科发展面临的挑战和机遇[J]. 第三军医大学学报, 2019, 41: 1815-1817.
[3]	Xing F, Yang L. Robust nucleus/cell detection and segmentation in digital pathology and microscopy images: A comprehensive review[J]. IEEE Rev Biomed Eng, 2016, 9: 234-263. doi: 10.1109/RBME.2016.2515127
[4]	唐娇, 梁毅雄, 邹北骥, 等. 基于级联分类器的乳腺癌病理学图像中有丝分裂检测[J]. 计算机应用研究, 2016, 33: 3876-3879. doi: 10.3969/j.issn.1001-3695.2016.12.079
[5]	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, 2016: 2487-2496.
[6]	Zheng Y, Jiang Z, Xie F, et al. Diagnostic regions attention network (dra-net) for histopathology wsi recommendation and retrieval[J]. IEEE Trans Med Imaging, 2021, 40: 1090-1103. doi: 10.1109/TMI.2020.3046636
[7]	Zhou SK, Greenspan H, Davatzikos C, et al. A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises[J]. arXiv preprint arXiv, 2020: 2008.09104.
[8]	Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42: 60-88. doi: 10.1016/j.media.2017.07.005
[9]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[J]. arXiv preprint arXiv, 2014: 1409.1556.
[10]	He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]. Proceedings of the IEEE Conference on Computer Cision and Pattern Recognition, 2016: 770-778.
[11]	Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826.
[12]	Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4700-4708.
[13]	Chollet F. Xception: Deep learning with depthwise separable convolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 1251-1258.
[14]	Howard A, Sandler M, Chu G, et al. Searching for mobilenetv3[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 1314-1324.
[15]	Schuster M, Paliwal KK. Bidirectional recurrent neural networks[M]. New Jersey, USA: IEEE Press, 1997.
[16]	徐冰冰, 岑科廷, 黄俊杰, 等. 图卷积神经网络综述[J]. 计算机学报, 2020, 43: 755-780. doi: 10.11897/SP.J.1016.2020.00755
[17]	Carbonneau MA, Cheplygina V, Granger E, et al. Multiple instance learning: A survey of problem characteristics and applications[J]. Pattern Recognition, 2018, 77: 329-353. doi: 10.1016/j.patcog.2017.10.009
[18]	Zhou ZH. A brief introduction to weakly supervised learning[J]. Nat Sci Rev, 2018, 5: 44-53. doi: 10.1093/nsr/nwx106
[19]	Ilse M, Tomczak J, Welling M. Attention-based deep multiple instance learning[C]. International conference on machine learning PMLR, 2018: 2127-2136.
[20]	Spanhol FA, Oliveira LS, Petitjean C, et al. Breast cancer histopathological image classification using convolutional neural networks[C] International Joint Conference on Neural Networks, 2016: 717-726.
[21]	Bayramoglu N, Kannala J, Heikkilä J. Deep learning for magnification independent breast cancer histopathology image classification[C]. International Conference on Pattern Recognition, 2017: 2440-2445.
[22]	Araújo T, Aresta G, Castro E, et al. Classification of breast cancer histology images using convolutional neural networks[J]. PLoS One, 2017, 12: e0177544. doi: 10.1371/journal.pone.0177544
[23]	Vesal S, Ravikumar N, Davari AA, et al. Classification of breast cancer histology images using transfer learning[C]. International Conference Image Analysis and Recognition, 2018: 812-819.
[24]	Vang YS, Chen Z, Xie X. Deep learning framework for multi-class breast cancer histology image classification[C]. International Conference Image Analysis and Recognition, 2018: 914-922.
[25]	Rakhlin A, Shvets A, Iglovikov V, et al. Deep convolutional neural networks for breast cancer histology image analysis[C]. International Conference Image Analysis and Recognition, 2018: 737-744.
[26]	Yan R, Li J, Rao X, et al. Nanet: Nuclei-aware network for grading of breast cancer in he stained pathological images[C]. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020: 865-870.
[27]	Shaban M, Awan R, Fraz MM, et al. Context-aware convolutional neural network for grading of colorectal cancer histology images[J]. IEEE Transactions on Medical Imaging, 2020: 2395-2405.
[28]	Yan R, Ren F, Wang Z, et al. Breast cancer histopathological image classification using a hybrid deep neural network[J]. Methods, 2020, 173: 52-60. doi: 10.1016/j.ymeth.2019.06.014
[29]	Zhou Y, Graham S, Alemi Koohbanani N, et al. Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images[C]. Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019.
[30]	Wang X, Chen H, Gan C, et al. Weakly supervised deep learning for whole slide lung cancer image analysis[J]. IEEE Trans Cybern, 2019, 50: 3950-3962. http://www.ncbi.nlm.nih.gov/pubmed/31484154
[31]	Nagpal K, Foote D, Tan F, et al. Development and validation of a deep learning algorithm for gleason grading of prostate cancer from biopsy specimens[J]. JAMA Oncol, 2020, 6: 1372-1380. doi: 10.1001/jamaoncol.2020.2485
[32]	Chen H, Han X, Fan X, et al. Rectified cross-entropy and upper transition loss for weakly supervised whole slide image classifier[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019: 351-359.
[33]	Wang S, Zhu Y, Yu L, et al. Rmdl: Recalibrated multi-instance deep learning for whole slide gastric image classification[J]. Med Image Anal, 2019, 58: 101549. doi: 10.1016/j.media.2019.101549
[34]	Chikontwe P, Kim M, Nam SJ, et al. Multiple instance learning with center embeddings for histopathology classification[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020: 519-528.
[35]	Raju A, Yao J, Haq MM, et al. Graph attention multi-instance learning for accurate colorectal cancer staging[C]. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2020: 529-539.
[36]	Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images[J]. Nat Med, 2019, 25: 1301-1309. doi: 10.1038/s41591-019-0508-1
[37]	Spanhol FA, Oliveira LS, Petitjean C, et al. A dataset for breast cancer histopathological image classification[J]. IEEE Trans Biomed Eng, 2016, 63: 1455-1462. doi: 10.1109/TBME.2015.2496264
[38]	Aresta G, Araújo T, Kwok S, et al. Bach: Grand challenge on breast cancer histology images[J]. Med Image Anal, 2019, 56: 122-139. doi: 10.1016/j.media.2019.05.010
[39]	Coudray N, Ocampo PS, Sakellaropoulos T, et al. Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning[J]. Nat Med, 2018, 24: 1559-1567. doi: 10.1038/s41591-018-0177-5
[40]	Adnan M, Kalra S, Tizhoosh HR. Representation learning of histopathology images using graph neural networks[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 202: 988-989.

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Research Progress of Cancer Classification Based on Deep Learning and Histopathological Images

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