Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data
- DOI
- 10.1007/s00330-023-09769-9
- Published
- 2023-06-07
- Container
- European Radiology
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s00330-023-09769-9,
title = {Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data},
author = {Brendan S. Kelly and Prateek Mathur and Jan Plesniar and Aonghus Lawlor and Ronan P. Killeen},
year = {2023},
journal = {European Radiology},
doi = {10.1007/s00330-023-09769-9},
url = {https://doi.org/10.1007/s00330-023-09769-9}
}RIS
TY - JOUR TI - Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data AU - Brendan S. Kelly AU - Prateek Mathur AU - Jan Plesniar AU - Aonghus Lawlor AU - Ronan P. Killeen PY - 2023 JO - European Radiology DO - 10.1007/s00330-023-09769-9 UR - https://doi.org/10.1007/s00330-023-09769-9 ER -
APA
Kelly, B. S., Mathur, P., Plesniar, J., Lawlor, A., & Killeen, R. P. (2023). Using deep learning–derived image features in radiologic time series to make personalised predictions: proof of concept in colonic transit data. European Radiology. https://doi.org/10.1007/s00330-023-09769-9
Source records
- crossref · retrieved 2026-09-26T11:10:09.291Z