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Predicting Prostate Cancer Directly from Tissue Images using Deep Learning on Mass Spectrometry Imaging and Whole Slide Imaging Data

Published online by Cambridge University Press:  22 July 2022

Md Inzamam Ul Haque
Affiliation:
The Bredesen Center, University of Tennessee, Knoxville, TN, USA
Debangshu Mukherjee
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Sylwia A. Stopka
Affiliation:
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
Nathalie Y.R. Agar
Affiliation:
Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA
Jacob Hinkle*
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
Olga S. Ovchinnikova*
Affiliation:
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
*

Abstract

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Type
Surface and Subsurface Microscopy and Microanalysis of Physical and Biological Specimens
Copyright
Copyright © Microscopy Society of America 2022

References

Van de Plas, R et al. , Nat. Methods 12(4) (2015), p. 366. doi: 10.1038/nmeth.3296CrossRefGoogle Scholar
Vollnhals, F et al. , Anal. Chem. 89(20) (2017), p. 10702. doi: 10.1021/acs.analchem.7b01256CrossRefGoogle Scholar
Randall, EC et al. , Mol. Cancer Res. MCR 17(5), p. 1155. doi: 10.1158/1541-7786.MCR-18-1057CrossRefGoogle Scholar