An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation.
- DOI
- 10.1002/acm2.70336
- Published
- 2025 Dec
- Container
- Journal of applied clinical medical physics
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1002/acm2.70336,
title = {An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation.},
author = {Iwao Y and Shiotsuki K and Hashimoto F and Ochiai T and Kagawa T and Nagata R and Eto M and Hatanaka Y and Yoshida Y and Asayama Y},
year = {2025},
journal = {Journal of applied clinical medical physics},
doi = {10.1002/acm2.70336},
url = {https://doi.org/10.1002/acm2.70336}
}RIS
TY - JOUR TI - An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation. AU - Iwao Y AU - Shiotsuki K AU - Hashimoto F AU - Ochiai T AU - Kagawa T AU - Nagata R AU - Eto M AU - Hatanaka Y AU - Yoshida Y AU - Asayama Y PY - 2025 JO - Journal of applied clinical medical physics DO - 10.1002/acm2.70336 UR - https://doi.org/10.1002/acm2.70336 ER -
APA
Y, I., K, S., F, H., T, O., T, K., R, N., M, E., Y, H., Y, Y., & Y, A. (2025). An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation.. Journal of applied clinical medical physics. https://doi.org/10.1002/acm2.70336
Source records
- pubmed · retrieved 2026-09-26T17:31:06.349Z