A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation

Giaquinto, A.N., et al., Breast Cancer Statistics, 2022. 2022. 72(6): p. 524–541.

Cheng, H.-D., et al., Automated breast cancer detection and classification using ultrasound images: A survey. 2010. 43(1): p. 299-317.

Xian, M., et al., Automatic breast ultrasound image segmentation: A survey. 2018. 79: p. 340-355.

Xue, C., et al., Global guidance network for breast lesion segmentation in ultrasound images. 2021. 70: p. 101989.

Shareef, B., et al. Estan: Enhanced small tumor-aware network for breast ultrasound image segmentation. in Healthcare. 2022. MDPI.

Hu, Y., et al., Automatic tumor segmentation in breast ultrasound images using a dilated fully convolutional network combined with an active contour model. 2019. 46(1): p. 215-228.

Yap, M.H., et al., Automated breast ultrasound lesions detection using convolutional neural networks. 2017. 22(4): p. 1218-1226.

Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18. 2015. Springer.

Zhou, Z., et al., Unet++: Redesigning skip connections to exploit multiscale features in image segmentation. 2019. 39(6): p. 1856–1867.

Oktay, O., et al., Attention u-net: Learning where to look for the pancreas. 2018.

Lin, T.-Y., et al. Feature pyramid networks for object detection. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.

Li, C., et al., Weakly supervised mitosis detection in breast histopathology images using concentric loss. 2019. 53: p. 165-178.

Liu, x, et al., Transformer and convolutional based dual branch network for retinal vessel segmentation in OCTA images, Biomedical Signal Processing and Control, Volume 83, 104604, May 2023,

Chen, G., et al., C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation. 2022. 225: p. 107086.

Irfan, R., et al., Dilated semantic segmentation for breast ultrasonic lesion detection using parallel feature fusion. 2021. 11(7): p. 1212.

Vaswani, A., et al., Attention is all you need. 2017. 30.

Dosovitskiy, A., et al., An image is worth 16x16 words: Transformers for image recognition at scale. 2020.

Petit, O., et al. U-net transformer: Self and cross attention for medical image segmentation. in Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings 12. 2021. Springer.

Chen, J., et al., Transunet: Transformers make strong encoders for medical image segmentation. 2021.

Gao, Y., M. Zhou, and D.N. Metaxas. UTNet: a hybrid transformer architecture for medical image segmentation. in Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27–October 1, 2021, Proceedings, Part III 24. 2021. Springer.

Moraru, L., et al., Optimization of breast lesion segmentation in texture feature space approach. 2014. 36(1): p. 129-135.

Lotfollahi, M., et al., Segmentation of breast ultrasound images based on active contours using neutrosophic theory. 2018. 45: p. 205-212.

Nugroho, A., H.A. Nugroho, and L. Choridah. Active contour bilateral filter for breast lesions segmentation on ultrasound images. in 2015 International Conference on Science in Information Technology (ICSITech). 2015. IEEE.

Jumaat, A.K., et al., Segmentation of masses from breast ultrasound images using parametric active contour algorithm. 2010. 8: p. 640-647.

Gómez, W., et al. Active contours without edges applied to breast lesions on ultrasound. in XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010: May 27–30, 2010 Chalkidiki, Greece. 2010. Springer.

Huang, Y.-L., et al., Level set contouring for breast tumor in sonography. 2007. 20: p. 238-247.

Liu, B., et al., Probability density difference-based active contour for ultrasound image segmentation. 2010. 43(6): p. 2028-2042.

Gao, L., X. Liu, and W.J.j.o.a.M. Chen, Phase-and GVF-based level set segmentation of ultrasonic breast tumors. 2012. 2012.

Zhu, L., et al. A second-order subregion pooling network for breast lesion segmentation in ultrasound. in Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VI 23. 2020. Springer.

Mo, Y., et al., Hover-trans: Anatomy-aware hover-transformer for roi-free breast cancer diagnosis in ultrasound images. 2023.

Cao, H., et al. Swin-unet: Unet-like pure transformer for medical image segmentation. in European conference on computer vision. 2022. Springer.

Zhou, H.-Y., et al., nnformer: Interleaved transformer for volumetric segmentation. 2021.

Abhisheka, B., S.K. Biswas, and B.J.A.o.C.M.i.E. Purkayastha, A comprehensive review on breast cancer detection, classification and segmentation using deep learning. 2023. 30(8): p. 5023–5052.

Radak, M., et al., Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies. 2023. 149(12): p. 10473-10491.

Ramesh, S., et al., Segmentation and classification of breast cancer using novel deep learning architecture. 2022. 34(19): p. 16533-16545.

Rezaei, Z.J.E.S.w.A., A review on image-based approaches for breast cancer detection, segmentation, and classification. 2021. 182: p. 115204.

Yusoff, M., et al., Accuracy analysis of deep learning methods in breast cancer classification: A structured review. 2023. 13(4): p. 683.

Jiang, T., et al., A hybrid enhanced attention transformer network for medical ultrasound image segmentation. 2023. 86: p. 105329.

Chen, B., et al., Transattunet: Multi-level attention-guided u-net with transformer for medical image segmentation. 2023.

Lin, X., et al., The lighter the better: rethinking transformers in medical image segmentation through adaptive pruning. 2023.

Hu, J., L. Shen, and G. Sun. Squeeze-and-excitation networks. in Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.

Yeung, M., et al., Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation. 2022. 95: p. 102026.

Al-Dhabyani, W., et al., Dataset of breast ultrasound images. 2020. 28: p. 104863.

Huang, Q., et al., Segmentation of breast ultrasound image with semantic classification of superpixels. 2020. 61: p. 101657.

Zhuang, Z., et al., An RDAU-NET model for lesion segmentation in breast ultrasound images. 2019. 14(8): p. e0221535.

Chen, L.-C., et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. in Proceedings of the European conference on computer vision (ECCV). 2018.

Byra, M., et al., Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network. 2020. 61: p. 102027.

Yan, Y., et al., Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images. 2022. 72: p. 103299.

Lee, D.-H. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. in Workshop on challenges in representation learning, ICML. 2013. Atlanta.

Yang, L., et al. St++: Make self-training work better for semi-supervised semantic segmentation. in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2022.

Liu, X., Liu, J., Xu, X. et al. A robust detail preserving anisotropic diffusion for speckle reduction in ultrasound images. BMC Genomics 12 (Suppl 5), S14 (2011).

留言 (0)

沒有登入
gif