Physics-Informed Generative Framework to Unsupervised Biomechanical Parameter Estimation for Tool-Tissue Force Prediction from Laparoscopic Depth Maps.
- DOI
- 10.3390/bioengineering13080863
- Published
- 2026 Jul 26
- Container
- Bioengineering (Basel, Switzerland)
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/bioengineering13080863,
title = {Physics-Informed Generative Framework to Unsupervised Biomechanical Parameter Estimation for Tool-Tissue Force Prediction from Laparoscopic Depth Maps.},
author = {Bini F and Finti A and Manni G and Marinozzi F},
year = {2026},
journal = {Bioengineering (Basel, Switzerland)},
doi = {10.3390/bioengineering13080863},
url = {https://doi.org/10.3390/bioengineering13080863}
}RIS
TY - JOUR TI - Physics-Informed Generative Framework to Unsupervised Biomechanical Parameter Estimation for Tool-Tissue Force Prediction from Laparoscopic Depth Maps. AU - Bini F AU - Finti A AU - Manni G AU - Marinozzi F PY - 2026 JO - Bioengineering (Basel, Switzerland) DO - 10.3390/bioengineering13080863 UR - https://doi.org/10.3390/bioengineering13080863 ER -
APA
F, B., A, F., G, M., & F, M. (2026). Physics-Informed Generative Framework to Unsupervised Biomechanical Parameter Estimation for Tool-Tissue Force Prediction from Laparoscopic Depth Maps.. Bioengineering (Basel, Switzerland). https://doi.org/10.3390/bioengineering13080863
Source records
- pubmed · retrieved 2026-09-26T05:55:39.537Z