Open Access
| Issue |
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
|
|
|---|---|---|
| Article Number | 02011 | |
| Number of page(s) | 8 | |
| Section | Communication and Networking | |
| DOI | https://doi.org/10.1051/itmconf/20268202011 | |
| Published online | 04 February 2026 | |
- D. Kumar, A. Wong & D.A. Clausi. Lung nodule classification using deep features in CT images. In 2015 12th conference on computer and robot vision, 133–138 (2015). IEEE, https://doi.org/10.1109/CRV.2015.25 [Google Scholar]
- J. Hofmanninger, F. Prayer, J. Pan, S. Ro¨hrich, H. Prosch & G. Langs 2020. Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. European radiology experimental, 4, 1–13. Springer, https://doi.org/10.1186/s41747-020-00173-2 [Google Scholar]
- C. icek, A. Abdulkadir, S.S. Lienkamp, T.Brox, & O. Ronneberger 2016. 3D U-Net:learning dense volumetric segmentation from sparse annotation. In Medical Image Computing and Computer-Ass is ted Intervention, 424–432. Springer, https://arxiv.org/abs/1606.06650 [Google Scholar]
- L. Jiang, J. Ou, R. Liu, Y. Zou, T. Xie, H. Xiao & T. Bai 2023. Rmau-net: Residual multi-scale attention u-net for liver and tumor segmentation in ct images. Computers in Biology and Medicine, 158, 106838 (2023). Elsevier. [Google Scholar]
- O. Ronneberger, P. Fischer & T. Brox U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germa ny , October 5-9, 2015, proceedings, part III, 234–241 (2015). Springer, https://doi.org/10.1007/978-3-319-24574-4_28 [Google Scholar]
- O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y. Hammerla, B. Kainz, et al. 2018. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999, https://arxiv.org/abs/1804.03999 [Google Scholar]
- F. Isensee, P.F. Jaeger, S. Kohl, A. Petersen & K.H. Maier-Hein, nnU-Net: A self-configuring method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203–211 (2021). Nature Publishing Gro up, https://arxiv.org/abs/1809.10486 [Google Scholar]
- A. Hatamizadeh, Y. Tang, V. Nath, D. Yang, A. Myronenko, B. Landman, H.R Roth, & D. Xu 2022. Unetr: Transformers for 3d medical image segmentation. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, 574–584 (2022). [Google Scholar]
- H. Cao, Y. Wang, J. Chen, D. Jiang, X. Zhang, Q. Tian & M. Wang 2022. Swinunet: Unet-like pure transformer for medica l image segmentation. In European conference on computer vision, 205–218. Springer. https://arxiv.org/abs/2201.01266 [Google Scholar]
- Z. Ji, Z. Zhao, X. Zeng, J. Wang, L. Zhao, X. Zhang & I. Ganchev 2023. ResDSda U-Net: A novel U-net-bas ed residual network for segmentation of pulmonary nodules in lung CT images. IEEE access, 11, 87775–87789. IEEE, https://doi.org/10.1109/ACCESS.2023.3305270 [Google Scholar]
- F.I. Diakogiannis, F. Waldner, P. Caccetta & C. Wu 2020. ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 94–114. Elsevier. [Google Scholar]
- M. Murugappan, A.K. Bourisly, N. B. Prakash, M.G. Sumithra & U.R. Acharya 2023. Automated semantic lung segmentation in chest CT images using deep neural network. Neural Computing and Applications, 35(21), 15343–15364. [Google Scholar]
- A. Saood & I. Hatem. COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet. BMC Medical Imaging, 21, 1–10 (2021). Springer, https://doi.org/10.1186/s12880-020-00529-5 [Google Scholar]
- S. Wang, X. Kuang, Y. Zhu, W. Zhang & H. Zhang 2022. Deep 3D multi-scale dual path network for automatic lung nodule classification. International Journal of Biomedical Engineering and Technology, 39(2), 149–169 (2022). Inderscience Publishers (IEL). [Google Scholar]
- W. Liu, Y. Li & D. Huang. RA-UNet: An improved network model for image denoising. The Visual Computer, 40(6), 4319–4335 (2024). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.

