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pro vyhledávání: '"Jordan Schupbach"'
Publikováno v:
2022 IEEE AUTOTESTCON.
Publikováno v:
IJCNN
Quantifying uncertainty is critically important to many applications of predictive modeling. In this paper we apply a recently developed method that uses U-statistics as a basis for estimating uncertainty in ensemble regressors to the case of neural
Publikováno v:
Medical Imaging: Digital Pathology
In this study, we present an automated approach to classify prostate cancer (PCa) whole slide images (WSIs) as high or low cancer aggressiveness using features derived from persistent homology, a tool of topological data analysis (TDA). This extends
Autor:
Anna Schenfisch, Jordan Schupbach, Robin Belton, David L. Millman, Daniel Salinas, Samuel Micka, Lucia Williams, Rostik Mertz, Brittany Terese Fasy
The persistence diagram (PD) is an increasingly popular topological descriptor. By encoding the size and prominence of topological features at varying scales, the PD provides important geometric and topological information about a space. Recent work
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9e21cfa2769218407ff6edabb259035d
Research reported in this publication was supported by Institutional Development Awards (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Awards P20GM103474, 5U54GM104944, U54GM115371, and 5P20G
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3bb9e5673193491526acf3a6062a2bc5
https://doi.org/10.15788/letstalk
https://doi.org/10.15788/letstalk