Towards scalable cancer prognosis by aligning semi-supervised learning with survival modeling
- DOI
- 10.1016/j.media.2026.104347
- Published
- 2027-01
- Container
- Medical Image Analysis
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.media.2026.104347,
title = {Towards scalable cancer prognosis by aligning semi-supervised learning with survival modeling},
author = {Yi Huang and Linghan Cai and Ziliang Feng and Mengjie Zhao and Xiaoguang Tu and Zhiyuan Yuan and Zhikang Wang},
year = {2027},
journal = {Medical Image Analysis},
doi = {10.1016/j.media.2026.104347},
url = {https://doi.org/10.1016/j.media.2026.104347}
}RIS
TY - JOUR TI - Towards scalable cancer prognosis by aligning semi-supervised learning with survival modeling AU - Yi Huang AU - Linghan Cai AU - Ziliang Feng AU - Mengjie Zhao AU - Xiaoguang Tu AU - Zhiyuan Yuan AU - Zhikang Wang PY - 2027 JO - Medical Image Analysis DO - 10.1016/j.media.2026.104347 UR - https://doi.org/10.1016/j.media.2026.104347 ER -
APA
Huang, Y., Cai, L., Feng, Z., Zhao, M., Tu, X., Yuan, Z., & Wang, Z. (2027). Towards scalable cancer prognosis by aligning semi-supervised learning with survival modeling. Medical Image Analysis. https://doi.org/10.1016/j.media.2026.104347
Source records
- crossref · retrieved 2026-11-06T22:39:40.137Z