Wentao Xie
ABSTRACT
Background: Breast cancer is one of the greatest health threats to women worldwide. Mammography is an effective and inexpensive tool for breast cancer early detection. Mammography-based breast cancer screening requires a lot of manpower from professional experts. Thus, computer-aided diagnosis tools, especially accurate classifiers which can distinguish the breast masses from the background tissues, are needed. However, since the sample size of publicly available mammography data sets is relatively small, the performance of the published breast mass identification models was not great, and the models were not well-embraced by clinical practice due to their low interpretability. Methods: In this work, using two independent and well-known mammography data sets, the CBIS-DDSM and the INbreast, we proposed a novel patch generation method for data augmentation and negative case generation. We implemented two successful deep learning models, the ResNet and the ViT, to classify the generated mass and non-mass patches. We also proposed to apply the patch-level model to the full-view mammogram screening in a sliding window manner and visualize/interpret the prediction results using a heatmap so that the clinic practice could potentially benefit from the well-trained model. Result: For the CBIS-DDSM dataset, we compared the performance of the ResNet and the ViT with and without data augmentation. The F1 score is 0.91, 0.86, 0.85, and 0.70, respectively. We also evaluated our models using other metrices such as accuracy, precision, recall, and ROC curve. The results show that the ResNet model outperforms the ViT model. And the data augmentation improves the overall performance of the models. The similar conclusions are further supported using the independent INbreast data. Furthermore, we also explored to use probability-based heatmaps to visualize the potential mass regions in mammogram images. Conclusion: The study shows that our patch-level data augmentation is effective in improving the classification performance of the deep learning models. The comparable performance on the CBIS-DDSM data and the independent INbreast data demonstrates the generalizability of our methods. The proposed heatmap visualization tool increases the interpretability of our results and could be a potential approach for clinic utilization.
- Breast cancer statistics | Canadian Cancer Society. https://cancer.ca/en/cancer-information/cancer-types/breast/statistics. Accessed 22 Jan 2022.Google Scholar
- Oeffinger KC, Fontham ETH, Etzioni R, Herzig A, Michaelson JS, Shih YCT, Breast Cancer Screening for Women at Average Risk: 2015 Guideline Update From the American Cancer Society. JAMA. 2015;314:1599–614. doi:10.1001/JAMA.2015.12783.Google ScholarCross Ref
- Wang L. Early diagnosis of breast cancer. Sensors. 2017;17:1572.Google Scholar
- Mittal M, Deolia S, Agrawal A, Chaturvedi H, Agrawal G, Chhabra KG. Prevalence of breast imaging reporting and data system (BIRADS) categories and breast consistencies in Central India –A cross-sectional survey. J Fam Med Prim Care. 2021;10:3219. doi:10.4103/JFMPC.JFMPC_2494_20.Google ScholarCross Ref
- HD N, K T, A N, C B, BK C, L H. Screening for breast cancer: an update for the U.S. Preventive Services Task Force. Ann Intern Med. 2009;151:727. doi:10.7326/0003-4819-151-10-200911170-00009.Google ScholarCross Ref
- 6. Gao Y, Geras KJ, Lewin AA, Moy L. New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence. Am J Roentgenol. 2019;212:300–7. doi:10.2214/AJR.18.20392.Google ScholarCross Ref
- 7. Yu S, Guan L. A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Trans Med Imaging. 2000;19:115–26. doi:10.1109/42.836371.Google ScholarCross Ref
- 8. Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: a review. Med Phys. 2009;36:2052–68. doi:10.1118/1.3121511.Google ScholarCross Ref
- 9. Wu Y, Giger ML, Doi K, Vyborny CJ, Schmidt RA, Metz CE. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology. 1993;187:81–7. doi:10.1148/RADIOLOGY.187.1.8451441.Google ScholarCross Ref
- Vijayarajeswari R, Parthasarathy P, Vivekanandan S, Basha AA. Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. Measurement. 2019;146:800–5.Google Scholar
- Farruggia A, Magro R, Vitabile S. Bayesian network based classification of mammography structured reports. Int Conf Comput Med Appl. 2013.Google ScholarCross Ref
- Abdulla SH, Sagheer AM, Veisi H. Breast cancer segmentation using K-means clustering and optimized region-growing technique. Bull Electr Eng Informatics. 2022;11:158–67. doi:10.11591/EEI.V11I1.3458.Google ScholarCross Ref
- Mohanty AK, Senapati MR, Beberta S, Lenka SK. Texture-based features for classification of mammograms using decision tree. Neural Comput Appl. 2013;23:1011–7.Google Scholar
- Rajaguru H, Sannasi Chakravarthy SR. Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer. Asian Pac J Cancer Prev. 2019;20:3777. doi:10.31557/APJCP.2019.20.12.3777.Google ScholarCross Ref
- Sahiner B, Petrick N, Chan HP, Hadjiiski LM, Paramagul C, Helvie MA, Computer-aided characterization of mammographic masses: Accuracy of mass segmentation and its effects on characterization. IEEE Trans Med Imaging. 2001;20:1275–84.Google Scholar
- Agarwal R, Diaz O, Lladó X, Yap MH, Martí R. Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging. 2019;6:1. doi:10.1117/1.JMI.6.3.031409.Google ScholarCross Ref
- Shen R, Yao J, Yan K, Tian K, Jiang C, Zhou K. Unsupervised domain adaptation with adversarial learning for mass detection in mammogram. Neurocomputing. 2020;393:27–37.Google Scholar
- Singh VK, Rashwan HA, Romani S, Akram F, Pandey N, Sarker MMK, Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network. Expert Syst Appl. 2020;139:112855.Google Scholar
- Houssein EH, Emam MM, Ali AA, Suganthan PN. Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review. Expert Syst Appl. 2021;167:114161.Google Scholar
- Lee RS, Gimenez F, Hoogi A, Miyake KK, Gorovoy M, Rubin DL. A curated mammography data set for use in computer-aided detection and diagnosis research. Sci Data 2017 41. 2017;4:1–9. doi:10.1038/sdata.2017.177.Google ScholarCross Ref
- Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol. 2012;19:236–48. doi:10.1016/J.ACRA.2011.09.014.Google ScholarCross Ref
- Cha KH, Petrick N, Pezeshk A, Graff CG, Sharma D, Badal A, Evaluation of data augmentation via synthetic images for improved breast mass detection on mammograms using deep learning. J Med imaging (Bellingham, Wash). 2020;7:1. doi:10.1117/1.JMI.7.1.012703.Google ScholarCross Ref
- Jesneck JL, Lo JY, Baker JA. Breast mass lesions: computer-aided diagnosis models with mammographic and sonographic descriptors. Radiology. 2007;244:390–8. doi:10.1148/RADIOL.2442060712.Google ScholarCross Ref
- Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci Reports 2019 91. 2019;9:1–12. doi:10.1038/s41598-019-48995-4.Google ScholarCross Ref
- Al-masni MA, Al-antari MA, Park JM, Gi G, Kim TY, Rivera P, Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Comput Methods Programs Biomed. 2018;157:85–94.Google Scholar
- He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society; 2016. p. 770–8. doi:10.1109/CVPR.2016.90.Google ScholarCross Ref
- Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv Prepr. 2020. https://arxiv.org/abs/2010.11929v2. Accessed 22 Jan 2022.Google Scholar
- Minu George. Breast Density Estimation and Micro-Calcification Classification. Aberystwyth University; 2021.Google Scholar
- Akkus Z, Galimzianova A, Hoogi A, Rubin DL, Erickson BJ. Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions. J Digit Imaging. 2017;30:449–59. doi:10.1007/S10278-017-9983-4/TABLES/4.Google ScholarCross Ref
- Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: Trends & directions. Comput Biol Med. 2022;142:105221. doi:10.1016/J.COMPBIOMED.2022.105221.Google ScholarDigital Library
- Salama WM, Aly MH. Deep learning in mammography images segmentation and classification: Automated CNN approach. Alexandria Eng J. 2021;60:4701–9.Google Scholar
- Gouda N, Amudha J. Skin Cancer Classification using ResNet. In: 2020 IEEE 5th International Conference on Computing Communication and Automation. Institute of Electrical and Electronics Engineers Inc.; 2020. p. 536–41.Google ScholarCross Ref
- Al-Haija QA, Adebanjo A. Breast cancer diagnosis in histopathological images using ResNet-50 convolutional neural network. In: International Electronics and Mechatronics Conference. Institute of Electrical and Electronics Engineers Inc.; 2020.Google ScholarCross Ref
- Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Attention is all you need. In: Advances in neural information processing systems. 2017. p. 5998–6008.Google Scholar
- Deng J, Dong W, Socher R, Li L-J, Kai Li, Li Fei-Fei. ImageNet: A large-scale hierarchical image database. Institute of Electrical and Electronics Engineers (IEEE); 2010. p. 248–55.Google Scholar
- Contributors Mmc. OpenMMLab's Image Classification Toolbox and Benchmark. 2020.Google Scholar
- Boulanger M, Nunes JC, Chourak H, Largent A, Tahri S, Acosta O, Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review. Phys Medica Eur J Med Phys. 2021;89:265–81. doi:10.1016/J.EJMP.2021.07.027.Google ScholarCross Ref
Index Terms
- Patch-based deep learning models for breast mammographic mass classification
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