Development of Self-Supervised Learning with Dinov2-Distilled Models for Parasite Classification in Screening

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dc.contributor.authorNatchapon Pinetsuksai
dc.contributor.authorVeerayuth Kittichai
dc.contributor.authorRangsan Jomtarak
dc.contributor.authorKomgrit Jaksukam
dc.contributor.authorTeerawat Tongloy
dc.contributor.authorSiridech Boonsang
dc.contributor.authorSanthad Chuwongin
dc.date.accessioned2025-03-10T07:34:45Z
dc.date.available2025-03-10T07:34:45Z
dc.date.issued2023
dc.description.abstractAt 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.
dc.identifier.citation2023 15th International Conference on Information Technology and Electrical Engineering, ICITEE 2023
dc.identifier.doi10.1109/ICITEE59582.2023.10317719
dc.identifier.isbn979-835030446-6
dc.identifier.scopus2-s2.0-85179891077
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4564
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.holderScopus
dc.subjectBootstrap Your Own Latent (BYOL)
dc.subjectDinov2
dc.subjectObject classification
dc.subjectparasite eggs
dc.subjectSelf-supervised learning (SSL)
dc.titleDevelopment of Self-Supervised Learning with Dinov2-Distilled Models for Parasite Classification in Screening
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85179891077&doi=10.1109%2fICITEE59582.2023.10317719&partnerID=40&md5=7a15a0120755808f46d925e6cce45f93
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oaire.citation.startPage323
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