Abstract
Early screening for breast cancer is an effective tool to detect tumors and decrease mortality among women. However, COVID restrictions made screening difficult in recent years due to a decrease in screening tests, reduction of routine procedures, and their delay. This preliminary study aimed to investigate mass detection in a large-scale OMI-DB dataset with three Transfer Learning settings in the early screening. We considered a subset of the OMI-DB dataset consisting of 6,000 cases, where we extracted 3,525 images with masses of Hologic Inc. manufacturer. This paper proposes to use the RetinaNet model with ResNet50 backbone to detect tumors in Full-Field Digital Mammograms. The model was initialized with ImageNet weights, COCO weights, and from scratch. We applied True Positive Rate at False Positive per Image evaluation metric with Free-Response Receiver Operating Characteristic curve to visualize the distributions of the detections. The proposed framework obtained 0.93 TPR at 0.84 FPPI with COCO weights initialization. ImageNet weights gave comparable results of 0.93 at 0.84 FPPI and from scratch demonstrated 0.84 at 0.84 FPPI.
This work was supported by MIUR (Minister for Education, University and Research, Law 232/216, Department of Excellence).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Yu, X., Wang, S.-H.: Abnormality diagnosis in mammograms by transfer learning based on ResNet18. Fundam. Inform. 168, 219–230 (2019). https://doi.org/10.3233/fi-2019-1829
Monticciolo, D.L., et al.: 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, 1280–1288 (2021). https://doi.org/10.1016/j.jacr.2021.04.021
Monticciolo, D.L., et al.: Breast cancer screening for average-risk women: recommendations from the ACR commission on breast imaging. J. Am. Coll. Radiol. 14, 1137–1143 (2017). https://doi.org/10.1016/j.jacr.2017.06.001
D’Elia, C., Marrocco, C., Molinara, M., Tortorella, F.: Detection of clusters of microcalcifications in mammograms: a multi classifier approach. In: 2008 21st IEEE International Symposium on Computer-Based Medical Systems. IEEE (2008)
Bria, A., Marrocco, C., Karssemeijer, N., Molinara, M., Tortorella, F.: Deep cascade classifiers to detect clusters of microcalcifications. In: Tingberg, A., Lång, K., Timberg, P. (eds.) IWDM 2016. LNCS, vol. 9699, pp. 415–422. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-41546-8_52
Marrocco, C., Molinara, M., Tortorella, F.: Algorithms for detecting clusters of microcalcifications in mammograms. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 884–891. Springer, Heidelberg (2005). https://doi.org/10.1007/11553595_108
Savelli, B., Bria, A., Molinara, M., Marrocco, C., Tortorella, F.: A multi-context CNN ensemble for small lesion detection. Artif. Intell. Med. 103, 101749 (2020). https://doi.org/10.1016/j.artmed.2019.101749
Bria, A., et al.: Improving the automated detection of calcifications using adaptive variance stabilization. IEEE Trans. Med. Imaging 37, 1857–1864 (2018). https://doi.org/10.1109/tmi.2018.2814058
Marchesi, A., et al.: 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 (2017)
Halling-Brown, M.D., et al.: OPTIMAM mammography image database: a large-scale resource of mammography images and clinical data. Radiol. Artif. Intell. 3, e200103 (2021). https://doi.org/10.1148/ryai.2020200103
Agarwal, R., Díaz, O., Yap, M.H., Lladó, X., Martí, R.: Deep learning for mass detection in full field digital mammograms. Comput. Biol. Med. 121, 103774 (2020). https://doi.org/10.1016/j.compbiomed.2020.103774
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2009)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollar, P.: Focal loss for dense object detection. In: 2017 IEEE International Conference on Computer Vision (ICCV). IEEE (2017)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2014)
Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV). IEEE (2015)
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2017)
Zlocha, M., Dou, Q., Glocker, B.: Improving RetinaNet for CT lesion detection with dense masks from weak RECIST labels. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 402–410. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_45
Chen, J., et al.: Detection of cervical lesions in colposcopic images based on the RetinaNet method. Biomed. Sig. Process. Control 75, 103589 (2022). https://doi.org/10.1016/j.bspc.2022.103589
Swinburne, N.C., et al.: for the MSK mind consortium: semisupervised training of a brain MRI tumor detection model using mined annotations. Radiology 303, 80–89 (2022). https://doi.org/10.1148/radiol.210817
Adachi, M., et al.: Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics (Basel) 10, 330 (2020). https://doi.org/10.3390/diagnostics10050330
Kozegar, E., Soryani, M., Minaei, B., Domingues, I.: Assessment of a novel mass detection algorithm in mammograms. J. Cancer Res. Ther. 9, 592–600 (2013). https://doi.org/10.4103/0973-1482.126453
Akselrod-Ballin, A., et al.: Deep learning for automatic detection of abnormal findings in breast mammography. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 321–329. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_37
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)
Anitha, J., Peter, J.D., Pandian, S.I.A.: A dual stage adaptive thresholding (DuSAT) for automatic mass detection in mammograms. Comput. Comput. Methods Programs Biomed. 138, 93–104 (2017)
te Brake, G.M., Karssemeijer, N., Hendriks, J.H.C.L.: An automatic method to discriminate malignant masses from normal tissue in digital mammograms1. Phys. Med. Biol. 45, 2843–2857 (2000). https://doi.org/10.1088/0031-9155/45/10/308
Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Med. Image Anal. 37, 114–128 (2017)
Ribli, D., Horváth, A., Unger, Z., Pollner, P., Csabai, I.: Detecting and classifying lesions in mammograms with Deep Learning. Sci. Sci. Rep. 8 (2018)
Jung, H., et al.: Detection of masses in mammograms using a one-stage object detector based on a deep convolutional neural network. PLoS ONE 13, e0203355 (2018). https://doi.org/10.1371/journal.pone.0203355
Agarwal, R., Diaz, O., Lladó, X., Yap, M.H., Martí, R.: Automatic mass detection in mammograms using deep convolutional neural networks. J. Med. Imaging (Bellingham). 6, 1 (2019). https://doi.org/10.1117/1.jmi.6.3.031409
Acknowledgements
The authors acknowledge the OPTIMAM project and Cancer Research Technology for providing the images used in this study, the staff at Royal Surrey NHS Foundation Trust who developed OMI-DB, and the charity Cancer Research UK which funded the OPTIMAM project.
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). Ruth Kehali Kassahun holds an EACEA Erasmus+ grant for the master in Medical Imaging and Applications.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 Springer Nature Switzerland AG
About this paper
Cite this paper
Ryspayeva, M., Molinara, M., Bria, A., Marrocco, C., Tortorella, F. (2023). Transfer Learning in Breast Mass Detection on the OMI-DB Dataset: A Preliminary Study. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13643. Springer, Cham. https://doi.org/10.1007/978-3-031-37660-3_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-37660-3_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-37659-7
Online ISBN: 978-3-031-37660-3
eBook Packages: Computer ScienceComputer Science (R0)
