@inproceedings{630a6fa6384044789b77a3b619a7c3f8,
title = "Comparing architectural features between heuristically human-annotated and Artificial Intelligence (AI) generated tumor and satellite labels in early-stage oral cavity cancer",
abstract = "Utilizing Artificial Intelligence (AI) generated tissue maps for outcome prediction would aid in reducing the exhaustive workload on pathologists. But how quantitatively analogous are these maps to pathologist labeled maps must be studied. Another area that interested us was to understand how the {"}satellite tumor{"}definition in tissue label maps affects the features extracted. Our work was motivated from these ideas. This work aids in understanding the impact on feature values extracted when an automatic relabeling is applied on both hand-annotated and AI tumor maps This would be a first step towards investigating if the AI maps can be reliable for recurrence risk prediction in early stage oral cavity cancer patients.",
author = "Dhadma Balachandran and Margaret Brandwein-Weber and Jonathan Folmsbee and Scott Doyle",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; Medical Imaging 2021: Digital Pathology ; Conference date: 15-02-2021 Through 19-02-2021",
year = "2021",
doi = "10.1117/12.2581153",
language = "English",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Tomaszewski, \{John E.\} and Ward, \{Aaron D.\}",
booktitle = "Medical Imaging 2021",
address = "United States",
}