Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method Detection of Breast Cancer using Deep-Learning

Jafar Abdollahi (1), Nioosha Davari (2), Yasin Panahi (3), Mossa Gardaneh (4)
(1) Department of Computer Engineering, Ardabil Branch, Islamic Azad University, Ardabil, Iran; Genomedicx, Richmond Hill, Ontario, Canada, Iran, Islamic Republic of,
(2) Department of Life Science, Faculty of New Science and Technology, University of Tehran, Tehran, Iran, Iran, Islamic Republic of,
(3) Pharmacology & Toxicology department, School of pharmacy, Ardabil University of Medical Sciences, Ardabil, Iran, Iran, Islamic Republic of,
(4) Genomedicx, Richmond Hill, Ontario, Canada, Canada


Background: Metastasis is the main cause of death toll among breast cancer patients. Since current approaches for diagnosis of lymph node metastases are time-consuming, deep learning (DL) algorithms with more speed and accuracy are explored for effective alternatives.

Methods: A total of 220025 whole-slide pictures from patients’ lymph nodes were classified into two cohorts: testing and training. For metastatic cancer identification, we employed hybrid convolutional network models. The performance of our diagnostic system was verified using 57458 unlabeled images that utilized criteria that included accuracy, sensitivity, specificity, and P-value.

Results: The DL-based system that was automatically and exclusively capable of quantifying and identifying metastatic lymph nodes was engineered. Quantification was made with 98.84% accuracy. Moreover, the precision of VGG16 and Recall was 92.42% and 91.25%, respectively. Further experiments demonstrated that metastatic cancer differentiation levels could influence the recognition performance.

Conclusion: Our engineered diagnostic complex showed an elevated level of precision and efficiency for lymph node diagnosis. Our innovative DL-based system has a potential to simplify pathological screening for metastasis in breast cancer patients.

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DeSantis CE, Fedewa SA, Goding Sauer A, Kramer JL, Smith RA, Jemal A. Breast cancer statistics, 2015: Convergence of incidence rates between black and white women. CA: a cancer journal for clinicians. 2016 Jan;66(1):31-42. doi: 10.3322/caac.21320.

U.S. Breast Cancer Statistics. Available from:,rates%20have%20been%20steady%20in%20women...%20More%20. Updated in Feb 2021.

Nathanson SD, Rosso K, Chitale D, Burke M. Lymph Node Metastasis. In Introduction to Cancer Metastasis 2017 Jan 1 (pp. 235-261). Academic Press. doi: 10.1016/B978-0-12-804003-4.00013-X.

Veronesi U, Paganelli G, Viale G, Luini A, Zurrida S, Galimberti V, et al. A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer. New England Journal of Medicine. 2003 Aug 7;349(6):546-53. doi: 10.1056/NEJMoa012782.

Manca G, Rubello D, Tardelli E, Giammarile F, Mazzarri S, Boni G, et al. Sentinel lymph node biopsy in breast cancer: indications, contraindications, and controversies. Clinical nuclear medicine. 2016 Feb 1;41(2):126-33. doi: 10.1097/RLU.0000000000000985.

Okur O, Sagiroglu J, Kir G, Bulut N, Alimoglu O. Diagnostic accuracy of sentinel lymph node biopsy in determining the axillary lymph node metastasis. Journal of Cancer Research and Therapeutics. 2020 Oct 1;16(6):1265. doi: 10.4103/jcrt.JCRT_1122_19.

Takada K, Kashiwagi S, Asano Y, Goto W, Kouhashi R, Yabumoto A, et al. Prediction of lymph node metastasis by tumor-infiltrating lymphocytes in T1 breast cancer. BMC cancer. 2020 Dec;20(1):1-3. doi: 10.1186/s12885-020-07101-y.

Shinden Y, Ueo H, Tobo T, Gamachi A, Utou M, Komatsu H, et al. Rapid diagnosis of lymph node metastasis in breast cancer using a new fluorescent method with γ-glutamyl hydroxymethyl rhodamine green. Scientific reports. 2016 Jun 9;6(1):1-7. doi: 10.1038/srep27525.

Fujita K, Kamiya M, Yoshioka T, Ogasawara A, Hino R, Kojima R, et al. Rapid and accurate visualization of breast tumors with a fluorescent probe targeting α-mannosidase 2C1. ACS central science. 2020 Oct 29;6(12):2217-27. doi: 10.1021/acscentsci.0c01189.

Combi F, Andreotti A, Gambini A, Palma E, Papi S, Biroli A, et al. Application of OSNA Nomogram in Patients With Macrometastatic Sentinel Lymph Node: A Retrospective Assessment of Accuracy. Breast Cancer: Basic and Clinical Research. 2021 May;15:11782234211014796. doi: 10.1177/11782234211014796.

Escuin D, López-Vilaró L, Mora J, Bell O, Moral A, Pérez I, et al. Circulating microRNAs in Early Breast Cancer Patients and Its Association With Lymph Node Metastases. Frontiers in Oncology. 2021;11. doi: 10.3389/fonc.2021.627811.

Steiner DF, MacDonald R, Liu Y, Truszkowski P, Hipp JD, Gammage C, et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. The American journal of surgical pathology. 2018 Dec;42(12):1636. doi: 10.1097/PAS.0000000000001151.

Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, et al. Artificial intelligence in medical imaging of the breast. Frontiers in Oncology. 2021:2892. doi: 10.3389/fonc.2021.600557.

Geras KJ, Mann RM, Moy L. Artificial intelligence for mammography and digital breast tomosynthesis: current concepts and future perspectives. Radiology. 2019 Nov;293(2):246-59. doi: 10.1148/radiol.2019182627.

Ha R, Chang P, Karcich J, Mutasa S, Fardanesh R, Wynn RT, et al. Axillary lymph node evaluation utilizing convolutional neural networks using MRI dataset. Journal of Digital Imaging. 2018 Dec;31(6):851-6. doi: 10.1007/s10278-018-0086-7.

Li J, Zhou Y, Wang P, Zhao H, Wang X, Tang N, et al. Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer. Quantitative Imaging in Medicine and Surgery. 2021 Jun;11(6):2477. doi: 10.21037/qims-20-525.

Sultan LR, Schultz SM, Cary TW, Sehgal CM. Machine learning to improve breast cancer diagnosis by multimodal ultrasound. In2018 IEEE International Ultrasonics Symposium (IUS) 2018 Oct 22 (pp. 1-4). IEEE. doi: 10.1109/ULTSYM.2018.8579953.

Wu T, Sultan LR, Tian J, Cary TW, Sehgal CM. Machine learning for diagnostic ultrasound of triple-negative breast cancer. Breast cancer research and treatment. 2019 Jan;173(2):365-73. doi: 10.1007/s10549-018-4984-7.

Lee YW, Huang CS, Shih CC, Chang RF. Axillary lymph node metastasis status prediction of early-stage breast cancer using convolutional neural networks. Computers in Biology and Medicine. 2021 Mar 1;130:104206. doi: 10.1016/j.compbiomed.2020.104206

Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning. Radiology. 2020 Jan;294(1):19-28. doi: 10.1148/radiol.2019190372.

Zheng X, Yao Z, Huang Y, Yu Y, Wang Y, Liu Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nature communications. 2020 Mar 6;11(1):1-9. doi: 10.1038/s41467-020-15027-z.

Wang J, Liu Q, Xie H, Yang Z, Zhou H. Boosted efficientnet: Detection of lymph node metastases in breast cancer using convolutional neural networks. Cancers. 2021 Jan;13(4):661. doi: 10.3390/cancers13040661.

Munien C, Viriri S. Classification of hematoxylin and eosin-stained breast cancer histology microscopy images using transfer learning with EfficientNets. Computational Intelligence and Neuroscience. 2021 Oct;2021. doi: 10.1155/2021/5580914.

Yu X, Chen H, Liang M, Xu Q, He L. A transfer learning-based novel fusion convolutional neural network for breast cancer histology classification. Multimedia Tools and Applications. 2020 Oct 9:1-5. doi: 10.1007/s11042-020-09977-1.

Abdollahi J, Keshandehghan A, Gardaneh M, Panahi Y, Gardaneh M. Accurate detection of breast cancer metastasis using a hybrid model of artificial intelligence algorithm. Archives of Breast Cancer. 2020 Feb 29:22-8. doi: 0.32768/abc.20207122-28.

Abdollahi J, Moghaddam BN, Parvar ME. Improving diabetes diagnosis in smart health using genetic-based Ensemble learning algorithm. Approach to IoT Infrastructure. Future Gen Distrib Systems J. 2019;1:23-30. doi: 10.1007/s42044-022-00100-1.

Abdollahi J. A review of Deep learning methods in the study, prediction and management of COVID-19. Journal of Industrial Integration and Management 2020. Vol. 05, No. 04, pp.453-479. doi: 10.1142/S2424862220500268.

Abdollahi J, Nouri-Moghaddam B. Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction. Iran Journal of Computer Science. 2022 Mar 21:1-6. doi: 10.1007/s42044-022-00100-1.

Xue J, Pu Y, Smith J, Gao X, Wang C, Wu B. Identifying metastatic ability of prostate cancer cell lines using native fluorescence spectroscopy and machine learning methods. Scientific Reports. 2021 Jan 26;11(1):1-0. doi: 10.1038/s41598-021-81945-7.

Irshad H, Veillard A, Roux L, Racoceanu D. Methods for nuclei detection, segmentation, and classification in digital histopathology: a review—current status and future potential. IEEE reviews in biomedical engineering. 2013 Dec 20;7:97-114. doi: 10.1109/RBME.2013.2295804.

McCann MT, Ozolek JA, Castro CA, Parvin B, Kovacevic J. Automated histology analysis: Opportunities for signal processing. IEEE Signal Processing Magazine. 2014 Dec 4;32(1):78-87. doi: 10.1109/MSP.2014.2346443.

Veta M, Pluim JP, Van Diest PJ, Viergever MA. Breast cancer histopathology image analysis: A review. IEEE transactions on biomedical engineering. 2014 Jan 30;61(5):1400-11. doi: 10.1109/TBME.2014.2303852.

Chen CL, Chen CC, Yu WH, Chen SH, Chang YC, Hsu TI, et al. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nature communications. 2021 Feb 19;12(1):1-3. doi: 10.1038/s41467-021-21467-y.

Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, et al. Lymph node metastasis prediction from primary breast cancer US images using deep learning. Radiology. 2020 Jan;294(1):19-28. doi: 10.1148/radiol.2019190372.

Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J. Mitosis detection in breast cancer histology images with deep neural networks. International conference on medical image computing and computer-assisted intervention Med Image Comput Comput Assist Interv. 2013;16(Pt 2):411-8. doi: 10.1007/978-3-642-40763-5_51.

Albawi S, Mohammed TA, Al-Zawi S. Understanding of a convolutional neural network. international conference on engineering and technology (ICET) 2017 Aug 21 (pp. 1-6). IEEE. doi: 10.1109/ICEngTechnol.2017.8308186.

Han Z, Wei B, Zheng Y, Yin Y, Li K, Li S. Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific reports. 2017 Jun 23;7(1):1-0. doi: 10.1038/s41598-017-04075-z.

Salakhutdinov R, Hinton G. (2009). Deep boltzmann machines. Proceedings of AISTATS 2009 (pp. 448–455). PMLR.

Hinton G, Salakhutdinov R. An efficient learning procedure for deep Boltzmann machines. Neural Computation. 2012;24(8):1967-2006. doi: 10.1162/NECO_a_00311.

Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006 Jul 28;313(5786):504-7. doi: 10.1126/science.1127647.

Shaffie A, Soliman A, Ghazal M, Taher F, Dunlap N, Wang B, et al. A new framework for incorporating appearance and shape features of lung nodules for precise diagnosis of lung cancer. IEEE International Conference on Image Processing (ICIP) 2017 Sep 17 (pp. 1372-1376). IEEE. doi: 10.1109/ICIP.2017.8296506.

Kingma DP, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. 2013 Dec 20. doi: 10.48550/arXiv.1312.6114.

Wang YW, Chen CJ, Wang TC, Huang HC, Chen HM, Shih JY, et al. Multi-energy level fusion for nodal metastasis classification of primary lung tumor on dual energy CT using deep learning. Computers in biology and medicine. 2022 Feb 1;141:105185. doi: 10.1016/j.compbiomed.2021.105185.

Satoh Y, Imokawa T, Fujioka T, Mori M, Yamaga E, Takahashi K, et al. Deep learning for image classification in dedicated breast positron emission tomography (dbPET). Annals of Nuclear Medicine. 2022 Jan 27:1-0. doi: 10.1007/s12149-022-01719-7.

Erhan D, Courville A, Bengio Y, Vincent P. Why does unsupervised pre-training help deep learning?. InProceedings of the thirteenth international conference on artificial intelligence and statistics 2010 Mar 31 (pp. 201-208). JMLR Workshop and Conference Proceedings. doi: Not Available

Yu D, Deng L, Dahl G. Roles of pre-training and fine-tuning in context-dependent DBN-HMMs for real-world speech recognition. InProc. NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2010 Dec. sn. doi: Not Available

Lee C, Panda P, Srinivasan G, Roy K. Training deep spiking convolutional neural networks with stdp-based unsupervised pre-training followed by supervised fine-tuning. Frontiers in neuroscience. 2018 Aug 3;12:435. doi: 10.3389/fnins.2018.00435.

Abdollahi J, Amani F, Mohammadnia A, Amani P, Fattahzadeh-ardalani G. Using Stacking methods based Genetic Algorithm to predict the time between symptom onset and hospital arrival in stroke patients and its related factors. JBE. 2022;8(1):8-23.

Abdollahi J, Nouri-Moghaddam B. Feature selection for medical diagnosis: Evaluation for using a hybrid Stacked-Genetic approach in the diagnosis of heart disease. arXiv preprint arXiv:2103.08175. 2021 Mar 15. doi: 10.48550/arXiv.2103.08175.

Abdollahi J, Nouri-Moghaddam B, Ghazanfari M. Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases). arXiv preprint arXiv:2103.08182. 2021 Mar 15. doi: 10.48550/arXiv.2103.08182.


Jafar Abdollahi
Nioosha Davari
Yasin Panahi
Mossa Gardaneh (Primary Contact)
Abdollahi J, Davari N, Panahi Y, Gardaneh M. Detection of Metastatic Breast Cancer from Whole-Slide Pathology Images Using an Ensemble Deep-Learning Method: Detection of Breast Cancer using Deep-Learning. Arch Breast Cancer [Internet]. 2022 Apr. 13 [cited 2024 Jul. 16];9(3):364-76. Available from:

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