Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
Source abstract
Abstract
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Radiomics and Machine Learning in Medical Imaging · COVID-19 diagnosis using AI
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, Ecuador
Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Politécnica de Valencia, E-46022 Valencia, Spain
Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, Canada
Department of Systems Design Engineering, Physics, and Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada
References (100+)
Show source-provided references
1. Global cancer statistics10.3322/caac.21492 ↗
2. Interval cancers following breast cancer screening in Singaporean women10.1002/ijc.10636 ↗
3. Munir, K., Elahi, H., Ayub, A., Frezza, F., and Rizzi, A. (2019). Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers, 11.10.3390/cancers11091235 ↗
4. American Cancer Society (2019). Breast Cancer Facts and Figures 2019, American Cancer Society.
5. Involvement of Machine Learning for Breast Cancer Image Classification: A Survey10.1155/2017/3781951 ↗
6. Skandalakis, J.E. (2009). Embryology and anatomy of the breast. Breast Augmentation, Springer.10.1007/978-3-540-78948-2_1 ↗
7. Dheeba, J., and Singh, N.A. (2015). Computer aided intelligent breast cancer detection: Second opinion for radiologists—A prospective study. Computational Intelligence Applications in Modeling and Control, Springer.10.1007/978-3-319-11017-2_16 ↗
8. Methods Used in Computer-Aided Diagnosis for Breast Cancer Detection Using Mammograms: A Review10.1155/2020/9162464 ↗
9. Improvement in Radiologists?? Detection of Clustered Microcalcifications on Mammograms10.1097/00004424-199010000-00006 ↗
10. Cochrane review on screening for breast cancer with mammography10.1016/S0140-6736(01)06449-2 ↗
11. Breast MRI: Guidelines from the European Society of Breast Imaging10.1007/s00330-008-0863-7 ↗
12. Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: A review10.1016/j.clinimag.2012.09.024 ↗
13. Dedicated breast computed tomography: Basic aspects10.1118/1.4919441 ↗
14. Breast Cancer Mortality in Mammographic Screening in Europe: A Review of Incidence-Based Mortality Studies10.1258/jms.2012.012080 ↗
15. Mammography screening and breast cancer mortality in Australia: An aggregate cohort study10.1258/jms.2012.011127 ↗
16. The benefits and harms of breast cancer screening: An independent review10.1038/bjc.2013.177 ↗
17. Liu, C.-Y., Hsu, C.-Y., Chou, Y.-H., and Chen, C.-M. (2014, January 8–10). A multi-scale tumor detection algorithm in whole breast sonography incorporating breast anatomy and tissue morphological information. Proceedings of the 2014 IEEE Healthcare Innovation Conference (HIC), Seattle, WA, USA.10.1109/HIC.2014.7038907 ↗
18. Diagnostic Performance of Digital versus Film Mammography for Breast-Cancer Screening10.1056/NEJMoa052911 ↗
19. Individual and Combined Effects of Age, Breast Density, and Hormone Replacement Therapy Use on the Accuracy of Screening Mammography10.7326/0003-4819-138-3-200302040-00008 ↗
20. Performance assessment for radiologists interpreting screening mammography10.1002/sim.2633 ↗
21. Diagnostic Accuracy of Digital Mammography in Patients with Dense Breasts Who Underwent Problem-solving Mammography: Effects of Image Processing and Lesion Type110.1148/radiol.2261012024 ↗
22. Mammographic Density and the Risk and Detection of Breast Cancer10.1056/NEJMoa062790 ↗
23. Analysis of cancers missed at screening mammography10.1148/radiology.184.3.1509041 ↗
24. Performance of screening mammography among women with and without a first-degree relative with breast cancer10.7326/0003-4819-133-11-200012050-00009 ↗
25. Ertosun, M.G., and Rubin, D.L. (2015, January 9–12). Probabilistic visual search for masses within mammography images using deep learning. Proceedings of the 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Washington, DC, USA.10.1109/BIBM.2015.7359868 ↗
26. Contrast Enhancement in Dense Breast Images to Aid Clustered Microcalcifications Detection10.1007/s10278-005-6976-5 ↗
27. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27–30). Rethinking the Inception Architecture for Computer Vision. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.308 ↗
28. Effectiveness and cost-effectiveness of double reading of mammograms in breast cancer screening: Findings of a systematic review10.1054/brst.2001.0350 ↗
29. Radiology’s Achilles’ heel: Error and variation in the interpretation of the Röntgen image10.1259/bjr.70.839.9536897 ↗
30. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs10.1016/j.jfranklin.2006.09.003 ↗
31. Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection
32. Computer-Aided Detection and Diagnosis of Breast Cancer10.1016/S0033-8389(05)70197-4 ↗
33. Machine Learning in Medical Imaging10.1016/j.jacr.2017.12.028 ↗
34. Medical breast ultrasound image segmentation by machine learning10.1016/j.ultras.2018.07.006 ↗
35. Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods10.1016/j.ultrasmedbio.2015.11.016 ↗
36. Deep learning based classification of breast tumors with shear-wave elastography10.1016/j.ultras.2016.08.004 ↗
37. Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans10.1038/srep24454 ↗
38. Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images10.1109/TMI.2018.2872031 ↗
39. Detecting Cardiovascular Disease from Mammograms With Deep Learning10.1109/TMI.2017.2655486 ↗
40. Dhungel, N., Carneiro, G., and Bradley, A.P. (2017, January 18–21). Fully automated classification of mammograms using deep residual neural networks. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia.10.1109/ISBI.2017.7950526 ↗
41. Large scale deep learning for computer aided detection of mammographic lesions10.1016/j.media.2016.07.007 ↗
42. Survey of deep learning in breast cancer image analysis10.1007/s12530-019-09297-2 ↗
43. Utilization of computer-aided detection for digital screening mammography in the United States, 2008 to 201610.1016/j.jacr.2017.08.033 ↗
44. The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: A systematic review10.1177/0284185118770917 ↗
45. New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence10.2214/AJR.18.20392 ↗
46. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool10.1148/ryai.2020190208 ↗
47. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks10.1117/1.JMI.3.3.034501 ↗
48. Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks10.1109/JBHI.2017.2731873 ↗
49. Computer-aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks10.1016/j.cmpb.2020.105361 ↗
50. End-to-end breast ultrasound lesions recognition with a deep learning approach
51. Xu, B., Wang, N., Chen, T., and Li, M. (2015). Empirical evaluation of rectified activations in convolutional network. arXiv.
52. Deep learning and non-negative matrix factorization in recognition of mammograms
53. Deep learning10.1038/nature14539 ↗
54. Deep learning for healthcare: Review, opportunities and challenges10.1093/bib/bbx044 ↗
55. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning10.1109/TMI.2016.2528162 ↗
56. Deep Learning in Medical Imaging: General Overview10.3348/kjr.2017.18.4.570 ↗
57. Overview of deep learning in medical imaging10.1007/s12194-017-0406-5 ↗
58. Comparing the performance of image enhancement methods to detect microcalcification clusters in digital mammography
59. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement10.1016/j.ijsu.2010.02.007 ↗
60. Five steps to conducting a systematic review10.1177/014107680309600304 ↗
61. The mammographic image analysis society digital mammogram database
62. A deep learning framework for supporting the classification of breast lesions in ultrasound images10.1088/1361-6560/aa82ec ↗
63. De Oliveira, J.E., Deserno, T.M., and Araújo, A.D.A. (2008, January 29–31). Breast Lesions Classification applied to a reference database. Proceedings of the 2nd International Conference, Hammanet, Tunisia.
64. Inbreast: Toward a full-field digital mammographic database10.1016/j.acra.2011.09.014 ↗
65. Abdelhafiz, D., Yang, C., Ammar, R., and Nabavi, S. (2019). Deep convolutional neural networks for mammography: Advances, challenges and applications. BMC Bioinform., 20.10.1186/s12859-019-2823-4 ↗
66. Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network10.1016/j.bspc.2020.102027 ↗
67. A deep feature based framework for breast masses classification10.1016/j.neucom.2016.02.060 ↗
68. Representation learning for mammography mass lesion classification with convolutional neural networks10.1016/j.cmpb.2015.12.014 ↗
69. An automated confirmatory system for analysis of mammograms10.1016/j.cmpb.2015.09.019 ↗
70. Dataset of breast ultrasound images10.1016/j.dib.2019.104863 ↗
71. Tian, J.-W., Wang, Y., Huang, J.-H., Ning, C.-P., Wang, H.-M., Liu, Y., and Tang, X.-L. (2008). The Digital Database for Breast Ultrasound Image. Proceedings of 11th Joint Conference on Information Sciences (JCIS), Atlantis Press.10.2991/jcis.2008.70 ↗
72. Open access database of raw ultrasonic signals acquired from malignant and benign breast lesions10.1002/mp.12538 ↗
73. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20–25). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.10.1109/CVPR.2009.5206848 ↗
74. AI-based computer-aided diagnosis (AI-CAD): The latest review to read first10.1007/s12194-019-00552-4 ↗
75. Ophthalmic diagnosis using deep learning with fundus images—A critical review10.1016/j.artmed.2019.101758 ↗
76. Shih, F.Y. (2010). Image Processing and Pattern Recognition: Fundamentals and Techniques, John Wiley & Sons.10.1002/9780470590416 ↗
77. Biltawi, M., Al-Najdawi, N.I.J.A.D., and Tedmori, S.A.R.A. (2012, January 10–13). Mammogram enhancement and segmentation methods: Classification, analysis, and evaluation. Proceedings of the 13th International Arab Conference on Information Technology, Zarqa, Jordan.
78. Dabass, J., Arora, S., Vig, R., and Hanmandlu, M. (2019, January 10–11). Segmentation techniques for breast cancer imaging modalities—A review. Proceedings of the 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India.10.1109/CONFLUENCE.2019.8776937 ↗
79. Pectoral muscle segmentation: A review10.1016/j.cmpb.2012.10.020 ↗
80. Breast ultrasound image segmentation: A survey10.1007/s11548-016-1513-1 ↗
81. Ultrasound image segmentation: A survey10.1109/TMI.2006.877092 ↗
82. Image segmentation in digital mammography: Comparison of local thresholding and region growing algorithms10.1016/0895-6111(92)90145-Y ↗
83. A hybrid multi-scale model for thyroid nodule boundary detection on ultrasound images10.1016/j.cmpb.2006.09.006 ↗
84. Multiscale superpixel method for segmentation of breast ultrasound10.1016/j.compbiomed.2020.103879 ↗
85. Diagnosis of breast tumors with sonographic texture analysis using wavelet transform and neural networks10.1016/S0301-5629(02)00620-8 ↗
86. Automated breast cancer detection and classification using ultrasound images: A survey10.1016/j.patcog.2009.05.012 ↗
87. Hasan, H., and Tahir, N.M. (2010, January 21–23). Feature selection of breast cancer based on principal component analysis. Proceedings of the 2010 6th International Colloquium on Signal Processing & Its Applications, Mallaca City, Malaysia.10.1109/CSPA.2010.5545298 ↗
88. Computer-aided classification of mammographic masses and normal tissue: Linear discriminant analysis in texture feature space10.1088/0031-9155/40/5/010 ↗
89. Maglogiannis, I., Zafiropoulos, E., and Kyranoudis, C. (2006). Intelligent segmentation and classification of pigmented skin lesions in dermatological images. Hellenic Conference on Artificial Intelligence, Springer.10.1007/11752912_23 ↗
90. Jin, X., Xu, A., Bie, R., and Guo, P. (2006). Machine learning techniques and chi-square feature selection for cancer classification using SAGE gene expression profiles. International Workshop on Data Mining for Biomedical Applications, Springer.10.1007/11691730_11 ↗
91. Verma, K., Singh, B.K., Tripathi, P., and Thoke, A.S. (2015). Review of feature selection algorithms for breast cancer ultrasound image. New Trends in Intelligent Information and Database Systems, Springer.10.1007/978-3-319-16211-9_3 ↗
92. Sikorski, J. (2004, January 7). Identification of malignant melanoma by wavelet analysis. Proceedings of the Student/Faculty Research Day, CSIS, Pace University, New York, NY, USA.
93. Chiem, A., Al-Jumaily, A., and Khushaba, R.N. (2007, January 3–6). A novel hybrid system for skin lesion detection. Proceedings of the 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, Australia.10.1109/ISSNIP.2007.4496905 ↗
94. Pattern Classification of Nevus with Texture Analysis10.1002/tee.20246 ↗
95. Zhou, H., Chen, M., and Rehg, J.M. (July, January 28). Dermoscopic interest point detector and descriptor. Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, MA, USA.10.1109/ISBI.2009.5193307 ↗
96. Mammogram Mass Classification Using Support Vector Machine with Texture, Shape Features and Hierarchical Centroid Method10.1166/jmihi.2014.1312 ↗
97. Sonar, P., Bhosle, U., and Choudhury, C. (2017, January 28–29). Mammography classification using modified hybrid SVM-KNN. Proceedings of the 2017 International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India.10.1109/CSPC.2017.8305858 ↗
98. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms10.1016/j.neucom.2007.06.015 ↗
99. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making10.1155/2013/832509 ↗
100. Al-Hadidi, M.R., Alarabeyyat, A., and Alhanahnah, M. (September, January 31). Breast cancer detection using k-nearest neighbor machine learning algorithm. Proceedings of the 2016 9th International Conference on Developments in eSystems Engineering (DeSE), Liverpool, UK.10.1109/DeSE.2016.8 ↗