Natchapon PinetsuksaiVeerayuth KittichaiRangsan JomtarakKomgrit JaksukamTeerawat TongloySiridech BoonsangSanthad Chuwongin2025-03-102025-03-1020232023 15th International Conference on Information Technology and Electrical Engineering, ICITEE 2023979-835030446-610.1109/ICITEE59582.2023.103177192-s2.0-85179891077https://repository.dusit.ac.th//handle/123456789/4564At present, parasitic infections in humans, such as intestinal parasitic infections and soil-transmitted helminth (STH) infection, remain a public health concern, with screening methods that are simple but time-consuming and require parasitology experts. Microscopy images are increasingly being used to aid diagnosis but creating labels for supervised learning (SL) is a time-consuming, labor-intensive, and costly process. Self-supervised learning (SSL) is a deep learning approach that aims to train models to represent features in unlabeled datasets using automatically generated labels or annotations from the data itself, rather than explicitly labeled human-labeled labels. It is an appropriate method to address the challenges associated with the difficulty of labeling large datasets. A pretrained model that has learned useful data representations from an SSL task is fine-tuned using labeled data to perform well on a specific downstream task. DINOv2 is an SSL model based on the Vision Transformer (ViT) architecture. In this study, we aim to create a model for screening for helminth egg infection using a fine-tuned Dinov2 with a classification layer head to demonstrate that dataset sizes of 1% and 10% are sufficient when compared to SL model. Rather than SL, which requires a significant amount of human data labeling and is generally impractical, the model developed in this study is expected to be used in active surveillance in the future. © 2023 IEEE.Bootstrap Your Own Latent (BYOL)Dinov2Object classificationparasite eggsSelf-supervised learning (SSL)Conference paperScopus