Segmentation of breast tumors using cutting-edge semantic segmentation models

References

• Agarwal R, Diaz O, Lladó X, Martí R. 2018 . Mass detection in mammograms using pre-trained deep learning models. In: 14th international workshop on breast imaging (IWBI 2018). Vol. 10718. International Society for Optics and Photonics; p. 107181F.
   Google Scholar
• Arevalo J, González FA, Ramos-Pollán R, Oliveira JL, Lopez MAG. 2016. Representation learning for mammography mass lesion classification with convolutional neural networks. Comput Methods Programs Biomed. 127:248–257. doi:10.1016/j.cmpb.2015.12.014.
   Google Scholar
• Badrinarayanan V, Kendall A, Cipolla R. 2017. Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 39(12):2481–2495. doi:10.1109/TPAMI.2016.2644615.
   Google Scholar
• Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, … McQuaid S, Gray RT, Murray LJ, Coleman HG. 2017. QuPath: Open source software for digital pathology image analysis. Sci Rep. 7(1):1–7. doi:10.1038/s41598-017-17204-5.
   Google Scholar
• Boot T, Irshad H. 2020 . Diagnostic assessment of deep learning algorithms for detection and segmentation of lesion in mammographic images. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; p. 56–65.
   Google Scholar
• Chen LC, Papandreou G, Schroff F, Adam H. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv Preprint arXiv:1706.05587.
   Google Scholar
• Cordts M, Omran M, Ramos S, Rehfeld T, Enzweiler M, Benenson R, & Schiele B 2016. The cityscapes dataset for semantic urban scene understanding. In: Proceedings of the IEEE conference on computer vision and pattern recognition; p. 3213–3223.
   Google Scholar
• Dumoulin V, Visin F. 2016. A guide to convolution arithmetic for deep learning. arXiv Preprint arXiv:1603.07285.
   Google Scholar
• Ioffe S, Szegedy C. 2015 . Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: International conference on machine learning. PMLR; p. 448–456.
   Google Scholar
• Lai X, Yang W, Li R. 2020. DBT masses automatic segmentation using U-net neural networks. Computational and mathematical methods in medicine, 2020.
   Google Scholar
• Long J, Shelhamer E, & Darrell T. 2015. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; p. 3431–3440.
   Google Scholar
• Lotter W, Sorensen G, Cox D, Lotter W, Sorensen G, Cox D, 2017. A multi-scale CNN and curriculum learning strategy for mammogram classification. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer; p. 169–177.
   Google Scholar
• Militello C, Rundo L, Dimarco M, Orlando A, Conti V, Woitek R, … D’-Angelo I, Bartolotta TV, Russo G. 2022. Semi-Automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering. Biomed Signal Process Control. 71:103113. doi:10.1016/j.bspc.2021.103113.
   Google Scholar
• Mordang JJ, Janssen T, Bria A, Kooi T, Gubern-Mérida A, Karssemeijer N. 2016 . Automatic microcalcification detection in multi-vendor mammography using convolutional neural networks. In: International workshop on breast imaging. Cham: Springer; p. 35–42.
   Google Scholar
• Ribli D, Horváth A, Unger Z, Pollner P, Csabai I. 2018. Detecting and classifying lesions in mammograms with deep learning. Sci Rep. 8(1):1–7. doi:10.1038/s41598-018-22437-z.
   Google Scholar
• Ronneberger O, Fischer P, Brox T. 2015 . U-Net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; p. 234–241.
   Google Scholar
• Rundo L, Militello C, Vitabile S, Russo G, Sala E, Gilardi MC. 2020. A survey on nature-inspired medical image analysis: a step further in biomedical data integration. Fundam Inform. 171(1–4):345–365. doi:10.3233/FI-2020-1887.
   Google Scholar
• Shen L. 2017. End-To-End training for whole image breast cancer diagnosis using an all convolutional design. arXiv Preprint arXiv:1711.05775.
   Google Scholar
• Shen L, Margolies LR, Rothstein JH, Fluder E, McBride R, Sieh W. 2019. Deep learning to improve breast cancer detection on screening mammography. Sci Rep. 9(1):1–12. doi:10.1038/s41598-019-48995-4.
   Google Scholar
• Wong KC, Syeda-Mahmood T, Moradi M. 2018. Building medical image classifiers with very limited data using segmentation networks. Med Image Anal. 49:105–116. doi:10.1016/j.media.2018.07.010.
   Google Scholar
• Yi D, Sawyer RL, Cohn D III, Dunnmon J, Lam C, Xiao X, Rubin D. 2017. Optimizing and visualizing deep learning for benign/malignant classification in breast tumors. arXiv Preprint arXiv:1705.06362.
   Google Scholar
• Yu F, Koltun V. 2015. Multi-Scale context aggregation by dilated convolutions. arXiv Preprint arXiv:1511.07122.
   Google Scholar
• Zhang Y, Chan S, Park VY, Chang KT, Mehta S, Kim MJ, … Su MY. 2020. Automatic detection and segmentation of breast cancer on MRI using mask R-CNN trained on non–fat-sat images and tested on fat-sat images. Acad Radiol.
   Google Scholar