AWDS-net: automatic whole-field segmentation network for characterising diverse breast masses

References

• Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv preprint arXiv:1802.06955. https://doi.org/10.48550/arXiv.1802.06955.
   Google Scholar
• Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12), 2481–2495. https://doi.org/10.1109/TPAMI.2016.2644615
   PubMed Web of Science ®Google Scholar
• Chen, G., Li, L., Dai, Y., Zhang, J., & Yap, M. H. (2023). AAU-Net: An adaptive attention U-Net for breast lesions segmentation in ultrasound images. IEEE Transactions on Medical Imaging, 42(5), 1289–1300. https://doi.org/10.1109/TMI.2022.3226268
   PubMed Web of Science ®Google Scholar
• Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A. L., & Zhou, Y. (2021). Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306. https://doi.org/10.48550/arXiv.2102.04306
   Google Scholar
• Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2017). Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4), 834–848. https://doi.org/10.1109/TPAMI.2017.2699184
   PubMed Web of Science ®Google Scholar
• Chowdary, G. J., & Yoagarajah, P. (2023). EU-Net: Enhanced U-shaped network for breast mass segmentation. IEEE Journal of Biomedical and Health Informatics, 1–11. https://doi.org/10.1109/JBHI.2023.3266740
   Web of Science ®Google Scholar
• Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013, September 22–26). Mitosis detection in breast cancer histology images with deep neural networks. Medical image computing and computer-assisted intervention–MICCAI 2013: 16th international conference, Nagoya, Japan, September 22-26, 2013, Proceedings, Part II 16. 411–418. https://doi.org/10.1007/978-3-642-40763-5_51
   Google Scholar
• Di, J., Ma, S., Lian, J., & Wang, G. (2022, January 15–16 ). A U-Net network model for medical image segmentation based on improved skip connections. 14th international conference on measuring technology and mechatronics automation (ICMTMA), Changsha, People’s Republic of China, pp. 298–302. https://doi.org/10.1109/ICMTMA54903.2022.00064
   Google Scholar
• Duffy, S. W., Tabár, L., Yen, A. M. F., Dean, P. B., Smith, R. A., Jonsson, H., Törnberg, S., Chen, S. L., Chiu, S. Y., Fann, J. C., Ku, M. M., Wu, W. Y., Hsu, C., Chen, Y., Svane, G., Azavedo, E., Grundström, H., Sundén, P., Leifland, K., … Chen, T. H. (2020). Mammography screening reduces rates of advanced and fatal breast cancers: Results in 549,091 women. Cancer, 126(13), 2971–2979. https://doi.org/10.1002/cncr.32859
   PubMed Web of Science ®Google Scholar
• Guo, Y. N., Dong, M., Yang, Z., Gao, X., Wang, K., Luo, C., Ma Y., & Zhang, J. (2016). A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Computer Methods and Programs in Biomedicine, 130, 31–45. https://doi.org/10.1016/j.cmpb.2016.02.019
   PubMed Web of Science ®Google Scholar
• Heath, M., Bowyer, K., Moore R., & Philip Kegelmeyer W. (2000, June 11–14). The digital database for screening mammography. Proceedings of the 5th international workshop on digital mammography, 212–218.
   Google Scholar
• Kamba, M., Manabe, M., Wakamiya, S., Yada, S., Aramaki, E., Odani, S., & Miyashiro, I. (2021). Medical needs extraction for breast cancer patients from question and answer services: Natural language processing-based approach. JMIR Cancer, 7(4), e32005. https://doi.org/10.2196/32005
   PubMed Web of Science ®Google Scholar
• Li, Q., Xu, J., Yuan, R., Zhang, Y., & Feng, R. (2023, June 04-10). Scsgnet: Spatial-correlated and shape-guided network for breast mass segmentation. ICASSP 2023-2023 IEEE international conference on acoustics, speech and signal processing (ICASSP), 1–5. https://doi.org/10.1109/ICASSP49357.2023.10096410
   Google Scholar
• Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
   PubMed Web of Science ®Google Scholar
• Long, J., Shelhamer, E., & Darrell, T. (2015, June 07-12). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440. https://doi.org/10.1109/CVPR.2015.7298965
   Google Scholar
• Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022). TTCNN: A breast cancer detection and classification towards computer-aided diagnosis using digital mammography in early stages. Applied Sciences, 12(7), 3273. https://doi.org/10.3390/app12073273
   Google Scholar
• Mulley, A. G., Trimble, C., & Elwyn, G. (2012). Stop the silent misdiagnosis: Patients’ preferences matter. Bmj, 345(nov07 6), e6572–e6572. https://doi.org/10.1136/bmj.e6572
   PubMedGoogle Scholar
• Ng, K. H., & Muttarak, M. (2003). Advances in mammography have improved early detection of breast cancer. Journal-Hong Kong College of Radiologists, 6, 126–131.
   Google Scholar
• Oktay, O., Schlemper, J., Folgoc, L. L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., & Rueckert, D. (2018). Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999. https://doi.org/10.48550/arXiv.1804.03999.
   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 (pp. 234–241). https://doi.org/10.1007/978-3-319-24574-4_28
   Google Scholar
• Shareef, B., Xian, M., & Vakanski, A. (2020, April 03–07). Stan: Small tumor-aware network for breast ultrasound image segmentation. 2020 IEEE 17th international symposium on biomedical imaging (ISBI), 1–5. https://doi.org/10.1109/isbi45749.2020.9098691
   Google Scholar
• Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2020, June 13-19). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 11534–11542. https://doi.org/10.1109/CVPR42600.2020.01155
   Google Scholar
• Xu, C., Qi, Y., Wang, Y., Lou, M., Pi, J., & Ma, Y. (2022). ARF-Net: An adaptive receptive field network for breast mass segmentation in whole mammograms and ultrasound images. Biomedical Signal Processing and Control, 71, 103178. https://doi.org/10.1016/j.bspc.2021.103178
   Web of Science ®Google Scholar
• Yang, Z., Dong, M., Guo, Y., Gao, X., Wang, K., Shi, B., & Ma, Y. (2016). A new method of micro-calcifications detection in digitized mammograms based on improved simplified PCNN. Neurocomputing, 218, 79–90. https://doi.org/10.1016/j.neucom.2016.08.068
   Web of Science ®Google Scholar
• Yin, H., & Shao, Y. (2023). CFU-Net: A coarse-fine U-Net with multi-level attention for medical image segmentation. IEEE Transactions on Instrumentation and Measurement, 72, 1–12. https://doi.org/10.1109/TIM.2023.3293887
   PubMed Web of Science ®Google Scholar
• Zarbakhsh, P. (2023). Spatial attention mechanism and cascade feature extraction in a U-Net model for enhancing breast tumor segmentation. Applied Sciences, 13(15), 8758. https://doi.org/10.3390/app13158758
   Google Scholar
• Zebari, D. A., Zeebaree, D. Q., Abdulazeez, A. M., Haron, H., & Hamed, H. N. A. (2020). Improved threshold based and trainable fully automated segmentation for breast cancer boundary and pectoral muscle in mammogram images. Ieee Access, 8, 203097–203116. https://doi.org/10.1109/ACCESS.2020.3036072
   Web of Science ®Google Scholar
• Zhao, H., Niu, J., Meng, H., Wang, Y., Li, Q., & Yu, Z. (2022, July 11–15). Focal U-Net: A focal self-attention based U-Net for breast lesion segmentation in ultrasound images. 44th annual international conference of the IEEE engineering in medicine & biology society (EMBC), Glasgow, Scotland, United Kingdom, 1506–1511. https://doi.org/10.1109/EMBC48229.2022.9870824
   Google Scholar
• Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2019). Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Transactions on Medical Imaging, 39(6), 1856–1867. https://doi.org/10.1109/TMI.2019.2959609
   PubMed Web of Science ®Google Scholar