ICIP 2022 CHALLENGE ON PARASITIC EGG DETECTION AND CLASSIFICATION IN MICROSCOPIC IMAGES: DATASET, METHODS AND RESULTS

被引：9

作者：

Anantrasirichai, Nantheera ^{[1
]}

Chalidabhongse, Thanarat H. ^{[2
]}

Palasuwan, Duangdao ^{[3
]}

Naruenatthanaset, Korranat ^{[2
]}

Kobchaisawat, Thananop ^{[2
]}

Nunthanasup, Nuntiporn ^{[3
]}

Boonpeng, Kanyarat ^{[3
]}

Ma, Xudong ^{[1
]}

Achim, Alin ^{[1
]}

机构：

[1] Univ Bristol, Visual Informat Lab, Bristol, Avon, England

[2] Chulalongkorn Univ, Dept Comp Engn, Bangkok, Thailand

[3] Chulalongkorn Univ, Dept Clin Microscopy, Oxidat Red Cell Disorders Res Unit, Bangkok, Thailand

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP | 2022年

关键词：

parasitic egg; microscopic imaging; object detection; classification; deep learning;

D O I：

10.1109/ICIP46576.2022.9897267

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：4306 / 4310

页数：5

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