Quality Control of Whole Slide Images using the YOLO Concept

Kimia Hemmatirad, Morteza Babaie, Mehdi Afshari, Danial Maleki, Mahjabin Saiadi, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computational pathology applies computer vision algorithms on whole slide images. The digitization of tissue glass slides marks a significant change in the clinical diagnostic workflow. One of the challenges in digital pathology is the presence of artifacts such as tissue fold, air bubbles, and ink-markers on archived cases. These artifacts may affect the focus points in digital scanners, and their presence may negatively affect the quality of the output tissue image and the subsequent diagnosis. Manual review of whole slide images requires experts, and it is a laborious and time-consuming task. In this paper, we trained the YOLO-v4 (You-Only-Look-Once) model to detect air bubble edges, tissue folds, which can happen during slide preparation, and ink-marked tissue glass slides, which occur when pathologists highlight regions of interest on glass slides. Our method is not only fast but also highly accurate. The experiments showed 99.5 % IOU calculation (intersection over union, also called Jaccard Index) for locating artifacts.

Original languageEnglish (US)
Title of host publicationProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages282-287
Number of pages6
ISBN (Electronic)9781665468459
DOIs
StatePublished - 2022
Event10th IEEE International Conference on Healthcare Informatics, ICHI 2022 - Rochester, United States
Duration: Jun 11 2022Jun 14 2022

Publication series

NameProceedings - 2022 IEEE 10th International Conference on Healthcare Informatics, ICHI 2022

Conference

Conference10th IEEE International Conference on Healthcare Informatics, ICHI 2022
Country/TerritoryUnited States
CityRochester
Period6/11/226/14/22

Keywords

  • artifact removal
  • digital pathology
  • ink markers
  • tissue folds
  • YOLO model

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Health Informatics

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