Parasitic Egg Detection and Classification in Microscopic Images
- Submission Dates:
- 01/30/2022 to 05/31/2022
- Citation Author(s):
- Submitted by:
- Nantheera Anant...
- Last updated:
- Mon, 09/05/2022 - 00:43
- Data Format:
- Creative Commons Attribution
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 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 come out from the challenge.
Datasets contain 11 parasitic egg types. Each category has 1,000 images.
- category_id 0: Ascaris lumbricoides
- category_id 1: Capillaria philippinensis
- category_id 2: Enterobius vermicularis
- category_id 3: Fasciolopsis buski
- category_id 4: Hookworm egg
- category_id 5: Hymenolepis diminuta
- category_id 6: Hymenolepis nana
- category_id 7: Opisthorchis viverrine
- category_id 8: Paragonimus spp
- category_id 9: Taenia spp. egg
- category_id 10: Trichuris trichiura
Please visit the Challenge Homepage (https://icip2022challenge.piclab.ai/).
Results must be submitted to the Leaderboard at the Challenge Homepage (https://icip2022challenge.piclab.ai/submission/).
Example of submission result in JSON format (https://uob-my.sharepoint.com/:u:/g/personal/eexna_bristol_ac_uk/EZn8sDT...)
Labels of test dataset (https://uob-my.sharepoint.com/:u:/g/personal/eexna_bristol_ac_uk/EXx6syY...)
Please cite our paper for the usage 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.