---
license: mit
task_categories:
- image-classification
tags:
- parasitic
- microscopic
- classification
- detection
size_categories:
- 10K<n<100K
---
# ICIP 2022 Challenge: Parasitic Egg Detection and Classification

Parasitic infections have been recognised as one of the most significant causes of illnesses by WHO. Most infected persons shed cysts or eggs in their living environment, and unwittingly cause transmission of parasites to other individuals.
Diagnosis of intestinal parasites is usually based on direct examination in the laboratory, of which capacity is obviously limited. Targeting to automate routine faecal examination for parasitic diseases, this challenge aims to gather experts in the field to develop robust automated methods to detect and classify eggs of parasitic worms in a variety of microscopic images.
Participants will work with a large-scale dataset, containing 11 types of parasitic eggs from faecal smear samples. They are the main interest because of causing major diseases and illness in developing countries. We are open to any techniques used for parasitic egg recognition, ranging from conventional approaches based on statistical models to deep learning techniques.
Finally, the organisers expect a new collaboration to come out from the challenge.

## Instructions

The dataset contains **11 parasitic egg types**. Each category has **1,000 images**.

| `category_id` | Parasite Name                    |
|---------------|----------------------------------|
| 0             | Ascaris lumbricoides             |
| 1             | Capillaria philippinensis        |
| 2             | Enterobius vermicularis          |
| 3             | Fasciolopsis buski               |
| 4             | Hookworm egg                     |
| 5             | Hymenolepis diminuta             |
| 6             | Hymenolepis nana                 |
| 7             | Opisthorchis viverrine           |
| 8             | Paragonimus spp                  |
| 9             | Taenia spp. egg                  |
| 10            | Trichuris trichiura              |

## Resources

- 🔗 [Challenge Homepage](https://icip2022challenge.piclab.ai/)

## Citation

Please cite the following paper if you use the dataset or methods after the competition:

> N. Anantrasirichai, T. H. Chalidabhongse, D. Palasuwan, K. Naruenatthanaset, T. Kobchaisawat, N. Nunthanasup, K. Boonpeng, X. Ma and A. Achim, "**ICIP 2022 Challenge on Parasitic Egg Detection and Classification in Microscopic Images: Dataset, Methods and Results**," IEEE ICIP2022.