Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction.
- DOI
- 10.1186/s42492-026-00231-3
- Published
- 2026 Sep 3
- Container
- Visual computing for industry, biomedicine, and art
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1186/s42492-026-00231-3,
title = {Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction.},
author = {Huang H and Zhou H and Huang K and Yang J and Ying P and Lai P and Lin Y and Gao Y},
year = {2026},
journal = {Visual computing for industry, biomedicine, and art},
doi = {10.1186/s42492-026-00231-3},
url = {https://doi.org/10.1186/s42492-026-00231-3}
}RIS
TY - JOUR TI - Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction. AU - Huang H AU - Zhou H AU - Huang K AU - Yang J AU - Ying P AU - Lai P AU - Lin Y AU - Gao Y PY - 2026 JO - Visual computing for industry, biomedicine, and art DO - 10.1186/s42492-026-00231-3 UR - https://doi.org/10.1186/s42492-026-00231-3 ER -
APA
H, H., H, Z., K, H., J, Y., P, Y., P, L., Y, L., & Y, G. (2026). Lightweight Transformer-based knowledge distillation framework for high-dimensional spatiotemporal radiomics in breast cancer risk prediction.. Visual computing for industry, biomedicine, and art. https://doi.org/10.1186/s42492-026-00231-3
Source records
- pubmed · retrieved 2026-09-25T23:52:46.170Z