Open Access
| Issue |
BIO Web Conf.
Volume 204, 2025
International Conference on Advancing Science and Technologies in Health Science (IEM-HEALS 2025)
|
|
|---|---|---|
| Article Number | 01019 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/bioconf/202520401019 | |
| Published online | 12 December 2025 | |
- C.-B. Zhang, Y. Li, Y. Wang, Delving deep into label smoothing. IEEE Trans. Image Process. 30, 5984–5996 (2021). https://doi.org/10.1109/TIP.2021.3089942 [Google Scholar]
- X. Mei, Z. Liu, P. Robson, B. Marinelli, M. Huang, A. Doshi, A. Jacobi, C. Cao, K. Link, T. Yang, Y. Wang, H. Greenspan, T. Deyer, Z. Fayad, Y. Yang, RadImageNet: An open radiologic deep learning research dataset for effective transfer learning. Radiol. Artif. Intell. 4, e210315 (2022). https://doi.org/10.1148/ryai.210315 [Google Scholar]
- S. Kuiry, A. Das, M. Nasipuri, N. Das, Regularizing CNNs using confusion penalty based label smoothing for histopathology images. Comput. Intell. Commun. Bus. Anal. (CICBA 2024). Commun. Comput. Inf. Sci. 2367, 257–269 (2025). https://doi.org/10.1007/978-3-031-81339-9_22 [Google Scholar]
- S.A. Vasudeva, J. Dolz, H. Lombaert, GeoLS: Geodesic label smoothing for image segmentation. In: I. Oguz, J. Noble, X. Li, et al. (eds) Medical Imaging with Deep Learning. Proc. Mach. Learn. Res. 227, 468–478 (2024). https://doi.org/10.59275/j.melba.2025-c1d9 [Google Scholar]
- M. Lukasik, S. Bhojanapalli, A.K. Menon, S. Kumar, Does label smoothing mitigate label noise? In: Proc. 37th Int. Conf. Mach. Learn. (ICML) 119, 6448–6458 (2020). https://doi.org/10.48550/arXiv.2003.02819 [Google Scholar]
- R. Müller, S. Kornblith, G. Hinton, When does label smoothing help? Proc. 33rd Int. Conf. Neural Inf. Process. Syst. 422, 4694–4703 (2019). https://doi.org/10.48550/arXiv.1906.02629 [Google Scholar]
- A.S. Sambyal, U. Niyaz, S. Shrivastava, N.C. Krishnan, D.R. Bathula, LS+: Informed label smoothing for improving calibration in medical image classification. In: M.G. Linguraru et al. (eds) Med. Image Comput. Comput. Assist. Interv. – MICCAI 2024. Lect. Notes Comput. Sci. 15010, 513–523 (2024). https://doi.org/10.1007/978-3-031-72117-5_48 [Google Scholar]
- R. Gao, T. Li, Y. Tang, Z. Xu, M. Kammer, S. Antic, K. Sandler, F. Moldonado, T. Lasko, B. Landman, A comparative study of confidence calibration in deep learning: From computer vision to medical imaging. arXiv preprint arXiv:2206.08833 (2022). https://doi.org/10.48550/arXiv.2206.08833. [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.