Abstract
Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets.
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Adachi M, Fujioka T, Mori M et al (2020) Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics 10(5):330
Agarwal R, Diaz O, Lladó X et al (2019) Automatic mass detection in mammograms using deep convolutional neural networks. J Med Imaging 6(03):1
Agarwal R, Díaz O, Yap MH et al (2020) Deep learning for mass detection in full field digital mammograms. Comput Biol Med 121(103774):103774
Akselrod-Ballin A, Karlinsky L, Hazan A et al (2017) Deep learning for automatic detection of abnormal findings in breast mammography deep learning in medical image analysis and multimodal learning for clinical decision support. Springer International Publishing, Cham
Altaf M (2021) A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks. Math Biosci Eng 18:5029–5046. https://doi.org/10.3934/mbe.2021256
Anitha J, Peter JD, Pandian SIA (2017) A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms, comput. Comput’’, Comput Methods Programs Biomed 138:93–104
Ansar W, Shahid AR, Raza B et al (2020) Breast cancer detection and localization using mobilenet based transfer learning for mammograms intelligent computing systems. Springer International Publishing, Cham
Battisti F, Falini P, Gorini G et al (2022) Cancer screening programmes in italy during the COVID-19 pandemic: an update of a nationwide survey on activity volumes and delayed diagnoses: Cancer screening and covid-19 pandemic. Annali dell’Istituto Superiore Di Sanità 58(1):16–24
Betancourt Tarifa AS, Marrocco C, Molinara M et al (2023) Transformer-based mass detection in digital mammograms. J Ambient Intell Human Comput 14(3):2723–2737. https://doi.org/10.1007/s12652-023-04517-9
Bria A, Marrocco C, Molinara M, et al (2012) A ranking-based cascade approach for unbalanced data. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp 3439–3442
Bria A, Marrocco C, Karssemeijer N, et al (2016) Deep cascade classifiers to detect clusters of microcalcifications in breast imaging. Breast Imaging pp 415–422
Bria A, Marrocco C, Borges LR et al (2018) Improving the automated detection of calcifications using adaptive variance stabilization. IEEE Trans Med Imaging 37(8):1857–1864
Chan JJ, Sim Y, Ow SGW et al (2020) The impact of COVID-19 on and recommendations for breast cancer care: the singapore experience. Endocr Relat Cancer 27(9):R307–R327
Chen J, Li P, Xu T et al (2022) Detection of cervical lesions in colposcopic images based on the RetinaNet method. Biomed Signal Process Control 75(103589):103589
Chougrad H, Zouaki H, Alheyane O (2017) Convolutional neural networks for breast cancer screening: Transfer learning with exponential decay. arXiv preprint arXiv:1711.10752
Comelli A, Stefano A, Bignardi S et al (2020a) Tissue classification to support local active delineation of brain tumors. In: Zheng Y, Williams BM, Chen K (eds) Medical image understanding and analysis. Springer International Publishing, Cham
Comelli A, Stefano A, Coronnello C et al (2020b) Radiomics: a new biomedical workflow to create a predictive model. In: Papież BW, Namburete AIL, Yaqub M (eds) Medical Image Understanding and Analysis. Springer International Publishing, Cham, pp 280–293
D’Elia C, Marrocco C, Molinara M, et al (2008) Detection of clusters of microcalcifications in mammograms: a multi classifier approach. In: 21st IEEE International Symposium on Computer-Based Medical Systems
Deng J, Dong W, Socher R, et al (2009) ImageNet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE
Dhungel N, Carneiro G, Bradley AP (2017) A deep learning approach for the analysis of masses in mammograms with minimal user intervention, med. Image Anal 37:114–128
Falconi L, Perez M, Aguilar W, et al (2020) Transfer learning and fine tuning in mammogram BI-RADS classification. In: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS). IEEE
Ferlay J, Colombet M, Soerjomataram I et al (2019) Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods: GLOBOCAN 2018 sources and methods. Int J Cancer 144(8):1941–1953
Gathani T, Reeves G, Dodwell D et al (2022) Impact of the COVID-19 pandemic on breast cancer referrals and diagnoses in 2020 and 2021: a population-based study in england. Br J Surg 109(2):e29–e30
Girshick R (2015) Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV). IEEE
Girshick R, Donahue J, Darrell T, et al (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE
Halling-Brown MD, Looney PT, Patel MN et al (2014) The oncology medical image database (OMI-DB). In: Law MY, Cook TS (eds) SPIE Proceedings. SPIE
Halling-Brown MD, Warren LM, Ward D et al (2021) OPTIMAM mammography image database a large-scale resource of mammography images and clinical data. Radiol Artif Intell 3(1)
Jiang F, Liu H, Yu S, et al (2017) Breast mass lesion classification in mammograms by transfer learning. In: Proceedings of the 5th International Conference on Bioinformatics and Computational Biology. ACM, New York, NY, USA
Jung H, Kim B, Lee I et al (2018) Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS One 13(9):e0203355
Kozegar E, Soryani M, Minaei B et al (2013) Assessment of a novel mass detection algorithm in mammograms. J Cancer Res Ther 9(4):592–600
Le Bihan Benjamin C, Simonnet JA, Rocchi M et al (2022) Monitoring the impact of COVID-19 in france on cancer care: a differentiated impact. Sci Rep 12(1):4207
Lévy D, Jain A (2016) Breast mass classification from mammograms using deep convolutional neural networks. arXiv preprint arXiv:1612.00542
Lin TY, Maire M, Belongie S et al (2014) Microsoft COCO common objects in context computer vision - ECCV 2014. Springer International Publishing, Cham
Lin TY, Dollar P, Girshick R, et al (2017a) Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE
Lin TY, Goyal P, Girshick R, et al (2017b) Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE
Luther A, Agrawal A (2020) A practical approach to the management of breast cancer in the COVID-19 era and beyond. Ecancermedicalscience 14:1059
Mahmood T, Li J, Pei Y et al (2021) An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer learning. Biology (Basel) 10(9):859
Marchesi A, Bria A, Marrocco C, et al (2017) The effect of mammogram preprocessing on microcalcification detection with convolutional neural networks. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS). IEEE
Marrocco C, Molinara M, Tortorella F (2005) Algorithms for detecting clusters of microcalcifications in mammograms image analysis and processing - ICIAP 2005. Springer, Berlin Heidelberg, Berlin, Heidelberg
Molinara M, Marrocco C, Tortorella F (2013) Automatic segmentation of the pectoral muscle in mediolateral oblique mammograms. In: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp 506–509, https://doi.org/10.1109/CBMS.2013.6627852
Monticciolo DL, Newell MS, Hendrick RE et al (2017) Breast cancer screening for average-risk women: Recommendations from the ACR commission on breast imaging. J Am Coll Radiol 14(9):1137–1143
Monticciolo DL, Malak SF, Friedewald SM et al (2021) Breast cancer screening recommendations inclusive of all women at average risk: Update from the ACR and society of breast imaging. J Am Coll Radiol 18(9):1280–1288
Ribli D, Horváth A, Unger Z et al (2018) Detecting and classifying lesions in mammograms with deep learning, sci. Sci Rep 8(1):4165
Ruiz-Medina S, Gil S, Jimenez B et al (2021) Significant decrease in annual cancer diagnoses in spain during the COVID-19 pandemic: A real-data study. Cancers (Basel) 13(13):3215
Saber A, Sakr M, Abo-Seida OM et al (2021) A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9:71194–71209
Samala RK, Chan HP, Hadjiiski L et al (2019) Breast cancer diagnosis in digital breast tomosynthesis: Effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imaging 38(3):686–696
Savelli B, Bria A, Molinara M et al (2020) A multi-context cnn ensemble for small lesion detection. Artif Intell Med 103:1
Sechopoulos I, Teuwen J, Mann R (2020) Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art, seminars in cancer biology. Seminars Cancer Biol. https://doi.org/10.1016/j.semcancer.2020.06.002
Shen R, Yao J, Yan K et al (2020) Unsupervised domain adaptation with adversarial learning for mass detection in mammogram, neurocomputing. Neurocomputing 293:27
Swinburne NC, Yadav V, Kim J et al (2022) Semisupervised training of a brain MRI tumor detection model using mined annotations. Radiology 303(1):80–89
Te Brake GM, Karssemeijer N, Hendriks JHCL (2000) An automatic method to discriminate malignant masses from normal tissue in digital mammograms1. Phys Med Biol 45(10):2843–2857
Tsai HY, Chang YL, Shen CT et al (2020) Effects of the COVID-19 pandemic on breast cancer screening in taiwan. Breast 54:52–55
Valerio LM, Alves DHA, Cruz LF, et al (2019) DeepMammo: Deep transfer learning for lesion classification of mammographic images. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS). IEEE
Vanni G, Materazzo M, Pellicciaro M et al (2020) Breast cancer and COVID-19: The effect of fear on patients’ decision-making process. In Vivo 34(3 Suppl):1651–1659
Yu X, Wang SH (2019) Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundam Inform 168(2–4):219–230
Zlocha M, Dou Q, Glocker B (2019) Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels lecture notes in computer science. Springer International Publishing, Cham
Acknowledgements
The authors acknowledge the OPTIMAM project for providing the images used in this study, the staff at Surrey who developed OMI-DB, and Cancer Research Technology, which funded the OPTIMAM project through our charity, Cancer Research UK. This work was supported by MUR (Italian Ministry for University and Research) funding to AB, CM, and MM through the DIEI Department of Excellence 2018-2022 (law 232/2016) and to FT through the DIEM Department of Excellence 2023-2027 (law 232/2016). Marya Ryspayeva holds an EACEA Erasmus+ grant for the M.Sc. in Medical Imaging and Applications (MAIA). The EU partially supported this work in the NextGenerationEU plan through MUR Decree n. 1051 23.06.2022 “PNRR Missione 4 Componente 2 Investimento 1.5” - CUP H33C22000420001.
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Ryspayeva, M., Bria, A., Marrocco, C. et al. Transfer learning in breast mass detection and classification. J Ambient Intell Human Comput 15, 3587–3602 (2024). https://doi.org/10.1007/s12652-024-04835-6
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DOI: https://doi.org/10.1007/s12652-024-04835-6