과제정보
이 논문은 과학기술정보통신부의 소프트웨어 중심대학 지원사업의 지원을 받아 수행하였음(2017-0-00130).
참고문헌
- J. Yu, D. Yang, and H. Zhao, "FFANet: Feature fusion attention network to medical image segmentation," Biomedical Signal Processing and Control, Vol.69, pp.102-912, 2021. https://doi.org/10.1016/j.bspc.2021.102912
- Y. Hong, H. Pan, W. Sun, and Y. Jia, "Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes," arXiv preprint arXiv:2101.06085, 2021.
- H. Yan, C. Zhang, and M. Wu, "Lawin transformer: Improving semantic segmentation transformer with multiscale representations via large window attention," arXiv preprint arXiv:2201.01615, 2022.
- O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation," in International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.234-241, 2015.
- H. Wang, Y. Zhu, H. Adam, A. Yuille, and L.-C. Chen, "Max-deeplab: End-to-end panoptic segmentation with mask transformers," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5463-5474, 2021.
- S. Bhattacharya, S. Shahnawaz, and A. Bhattacharya, "ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation," arXiv preprint arXiv:2206.05225, 2022.
- Y. Chen, Y. Mo, A. Readie, G. Ligozio, T. Coroller, and B. W. Papiez, "VertXNet: Automatic segmentation and identification of lumbar and cervical vertebrae from spinal X-ray images," arXiv preprint arXiv:2207.05476, 2022.
- N. Tajbakhsh, L. Jeyaseelan, Q. Li, J. N. Chiang, Z. Wu, and X. Ding, "Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation," Medical Image Analysis, Vol.63, p.101-693, 2020. https://doi.org/10.1016/j.media.2020.101693
- J. Jiang et al., "Multiple resolution residually connected feature streams for automatic lung tumor segmentation for CT images," IEEE Transactions on Medical Imaging, Vol.38, No.1, pp.134-144, 2019. https://doi.org/10.1109/TMI.2018.2857800
- K. Sun et al., "High-resolution representations for labeling pixels and regions," arXiv preprint arXiv:1904.04514, 2019.
- Y. Lee, D. Lee, J. Jeong, H. Kim, and H. Kim, "Thoracic spine segmentation of X-ray images using a modified HRNet," in Proceedings of the Annual Spring Conference of Korea Information Processing Society Conference (KIPS) 2022, Vol.29, pp.705-707, 2022.
- S. Han, S. Hong, Y. Lee, D. Lee, K. Kim, and H. Kim, "A deep learning technique to automatically extract VHS from X-ray images," in Proceedings of Korea Computer Congress 2022 (KCC 2022), Vol.29, pp.2075-2077, 2022.
- Y. Yuan, X. Chen, and J. Wang, "Object-contextual representations for semantic segmentation," in European Conference on Computer Vision (ECCV), pp.173-190, 2020.
- J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.7132-7141, 2018.
- X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri and R. M. Summers, "ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2097-2106, 2017.