PathoVA: A visual analytics tool for pathology diagnosis and reporting

Alberto Corvò, Marc A. van Driel, Michel A. Westenberg

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
2 Downloads (Pure)

Abstract

We introduce PathoVA, a visual analytics system for computer aided pathology diagnosis. The diagnostic work of a pathologist involves characterizing cells and the appearance of histological sections of tissues, which are stained by techniques that highlight particular elements. Our system supports this work by recording activities in a digital tissue slide viewer, from which a diagnostic trace is constructed automatically. The visualization of the trace is enhanced with quantitative data about the tissue obtained by image analysis. Using the trace visualization, the pathologist can fill out the pathology report according to the required protocol. We demonstrate our approach on a use case of breast tissue examination. Our qualitative evaluation shows that PathoVA supports the diagnostic procedure and simplifies reporting.

Original languageEnglish
Title of host publication2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages77-83
Number of pages7
ISBN (Electronic)9781538631874
DOIs
Publication statusPublished - 15 Jun 2018
Event8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017 - Phoenix, United States
Duration: 1 Oct 2017 → …

Conference

Conference8th Annual IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017
CountryUnited States
CityPhoenix
Period1/10/17 → …

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Keywords

  • breast-cancer
  • computational pathology
  • computer assisted diagnosis
  • Digital pathology
  • Nottingham Score
  • pathology informatics
  • visual analytics
  • workflow

Cite this

Corvò, A., van Driel, M. A., & Westenberg, M. A. (2018). PathoVA: A visual analytics tool for pathology diagnosis and reporting. In 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017 (pp. 77-83). Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/VAHC.2017.8387544