A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology. Garzon, A. S., Verma, R., Chen, Y., Tovar, D. B., Castro, E. R., & Tiwari, P. In Tomaszewski, J. E., Ward, A. D., & Levenson, R. M., editors, Digital and Computational Pathology, volume 12039, of SPIE Proceedings, 2022. SPIE.
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Paper bibtex @inproceedings{conf/midp/GarzonVCTCT22,
added-at = {2024-12-24T00:00:00.000+0100},
author = {Garzon, Alvaro Sandino and Verma, Ruchika and Chen, Yijang and Tovar, David Becerra and Castro, Eduardo Romero and Tiwari, Pallavi},
biburl = {https://www.bibsonomy.org/bibtex/2d454bebfe1771632167b67cea8eb038d/dblp},
booktitle = {Digital and Computational Pathology},
crossref = {conf/midp/2022},
editor = {Tomaszewski, John E. and Ward, Aaron D. and Levenson, Richard M.},
ee = {https://doi.org/10.1117/12.2611855},
interhash = {d37b803ec76054f765a962f5ec862a5c},
intrahash = {d454bebfe1771632167b67cea8eb038d},
isbn = {9781510649545},
keywords = {dblp},
publisher = {SPIE},
series = {SPIE Proceedings},
timestamp = {2024-12-30T07:13:55.000+0100},
title = {A hierarchical deep learning approach for segmentation of glioblastoma tumor niches on digital histopathology.},
url = {http://dblp.uni-trier.de/db/conf/midp/midp2022.html#GarzonVCTCT22},
volume = 12039,
year = 2022
}
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