| EDITORIAL |
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| Year : 2020 | Volume
: 11
| Issue : 1 | Page : 7 |
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Value of public challenges for the development of pathology deep learning algorithms
Douglas Joseph Hartman1, Jeroen A. W. M. Van Der Laak2, Metin N Gurcan3, Liron Pantanowitz1
1 Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
2 Department of Pathology, Radboud University Medical Center, Nijmegen, The Netherlands; Centre for Medical Image Science and Visualisation, Linköping, Sweden
3 Center for Biomedical Informatics, Wake Forest School of Medicine, Winston Salem, NC, USA
Correspondence Address:
Dr. Douglas Joseph Hartman
Department of Pathology, University of Pittsburgh Medical Center, 200 Lothrop Street, A-610, Pittsburgh 15213, PA
USA
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jpi.jpi_64_19
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The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology. To date, 24 public challenges related to pathology tasks have been published. This article discusses these public challenges and briefly reviews the underlying characteristics of public challenges and why they are helpful to the development of digital tools.
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