ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 25 references that are in the map. First posted on arXiv 11 Aug 2022.
Builds on
9 of 25 references.
References · 9
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- 2021CBNet: A Composite Backbone Network Architecture for Object Detectioncited by 77
- 2020Artificial Intelligence in the Creative Industries: A Reviewcited by 61
- 2016Deep Convolutional Neural Networks for Microscopy-Based Point of Care Diagnosticscited by 6
- 2022Background Mixup Data Augmentation for Hand and Object-in-Contact Detectioncited by 6
- 2021Parasitic Egg Detection and Classification in Low-cost Microscopic Images using Transfer Learningcited by 3
- 2022Localization and Classification of Parasitic Eggs in Microscopic Images Using an EfficientDet Detectorcited by 2
- 2022Detection of Parasitic Eggs from Microscopy Images and the emergence of a new datasetcited by 1
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Citing papers per year: 2026: 1.
1 paper.