Journal of Pathology Informatics Journal of Pathology Informatics
Contact us | Home | Login   |  Users Online: 349  Print this pageEmail this pageSmall font sizeDefault font sizeIncrease font size 

Year : 2020  |  Volume : 11  |  Issue : 1  |  Page : 7

Value of public challenges for the development of pathology deep learning algorithms

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
Login to access the Email id

Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jpi.jpi_64_19

Rights and Permissions

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.

Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)

 Article Access Statistics
    PDF Downloaded470    
    Comments [Add]    
    Cited by others 12    

Recommend this journal