End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study
- DOI
- 10.1016/s2589-7500(23)00208-x
- Published
- 2024-01
- Container
- The Lancet Digital Health
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/s2589-7500-23-00208-x,
title = {End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study},
author = {Xiaofeng Jiang and Michael Hoffmeister and Hermann Brenner and Hannah Sophie Muti and Tanwei Yuan and Sebastian Foersch and Nicholas P West and Alexander Brobeil and Jitendra Jonnagaddala and Nicholas Hawkins and Robyn L Ward and Titus J Brinker and Oliver Lester Saldanha and Jia Ke and Wolfram Müller and Heike I Grabsch and Philip Quirke and Daniel Truhn and Jakob Nikolas Kather},
year = {2024},
journal = {The Lancet Digital Health},
doi = {10.1016/s2589-7500(23)00208-x},
url = {https://doi.org/10.1016/s2589-7500(23)00208-x}
}RIS
TY - JOUR TI - End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study AU - Xiaofeng Jiang AU - Michael Hoffmeister AU - Hermann Brenner AU - Hannah Sophie Muti AU - Tanwei Yuan AU - Sebastian Foersch AU - Nicholas P West AU - Alexander Brobeil AU - Jitendra Jonnagaddala AU - Nicholas Hawkins AU - Robyn L Ward AU - Titus J Brinker AU - Oliver Lester Saldanha AU - Jia Ke AU - Wolfram Müller AU - Heike I Grabsch AU - Philip Quirke AU - Daniel Truhn AU - Jakob Nikolas Kather PY - 2024 JO - The Lancet Digital Health DO - 10.1016/s2589-7500(23)00208-x UR - https://doi.org/10.1016/s2589-7500(23)00208-x ER -
APA
Jiang, X., Hoffmeister, M., Brenner, H., Muti, H. S., Yuan, T., Foersch, S., West, N. P., Brobeil, A., Jonnagaddala, J., Hawkins, N., Ward, R. L., Brinker, T. J., Saldanha, O. L., Ke, J., Müller, W., Grabsch, H. I., Quirke, P., Truhn, D., & Kather, J. N. (2024). End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. The Lancet Digital Health. https://doi.org/10.1016/s2589-7500(23)00208-x
Source records
- crossref · retrieved 2026-09-26T11:24:31.374Z