Comparing architectural features between heuristically human-annotated and Artificial Intelligence (AI) generated tumor and satellite labels in early-stage oral cavity cancer

Dhadma Balachandran; Margaret Brandwein-Weber; Jonathan Folmsbee; Scott Doyle

doi:10.1117/12.2581153

15 February 2021 Comparing architectural features between heuristically human-annotated and Artificial Intelligence (AI) generated tumor and satellite labels in early-stage oral cavity cancer

Dhadma Balachandran, Margaret Brandwein-Weber, Jonathan Folmsbee, Scott Doyle

Author Affiliations +

Proceedings Volume 11603, Medical Imaging 2021: Digital Pathology; 116030X (2021) https://doi.org/10.1117/12.2581153
Event: SPIE Medical Imaging, 2021, Online Only

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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.

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Dhadma Balachandran, Margaret Brandwein-Weber, Jonathan Folmsbee, and Scott Doyle "Comparing architectural features between heuristically human-annotated and Artificial Intelligence (AI) generated tumor and satellite labels in early-stage oral cavity cancer", Proc. SPIE 11603, Medical Imaging 2021: Digital Pathology, 116030X (15 February 2021); https://doi.org/10.1117/12.2581153

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