ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results

Nantheera Anantrasirichai, Thanarat H. Chalidabhongse, Duangdao Palasuwan, Korranat Naruenatthanaset, Thananop Kobchaisawat, Nuntiporn Nunthanasup, Kanyarat Boonpeng, Xudong Ma, Alin Achim

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

7 Citations (Scopus)
54 Downloads (Pure)

Abstract

Manual examination of faecal smear samples to identify the existence of parasitic eggs is very time-consuming and can only be done by specialists. Therefore, an automated system is required to tackle this problem since it can relate to serious intestinal parasitic infections. This paper reviews the ICIP 2022 Challenge on parasitic egg detection and classification in microscopic images. We describe a new dataset for this application, which is the largest dataset of its kind. The methods used by participants in the challenge are summarised and discussed along with their results.
Original languageEnglish
Title of host publication2022 IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISBN (Electronic)978-1-6654-9620-9
ISBN (Print)978-1-6654-9621-6
DOIs
Publication statusPublished - 18 Oct 2022

Publication series

Name Proceedings - International Conference on Image Processing
PublisherIEEE
ISSN (Print)1522-4880
ISSN (Electronic)2381-8549

Bibliographical note

The 29th IEEE International Conference on Image Processing

Keywords

  • cs.CV

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