Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification
High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-superv...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140204/ https://www.ncbi.nlm.nih.gov/pubmed/35626392 http://dx.doi.org/10.3390/diagnostics12051237 |
_version_ | 1784715041539358720 |
---|---|
author | Taleb, Aiham Rohrer, Csaba Bergner, Benjamin De Leon, Guilherme Rodrigues, Jonas Almeida Schwendicke, Falk Lippert, Christoph Krois, Joachim |
author_facet | Taleb, Aiham Rohrer, Csaba Bergner, Benjamin De Leon, Guilherme Rodrigues, Jonas Almeida Schwendicke, Falk Lippert, Christoph Krois, Joachim |
author_sort | Taleb, Aiham |
collection | PubMed |
description | High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ≥45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive. |
format | Online Article Text |
id | pubmed-9140204 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91402042022-05-28 Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification Taleb, Aiham Rohrer, Csaba Bergner, Benjamin De Leon, Guilherme Rodrigues, Jonas Almeida Schwendicke, Falk Lippert, Christoph Krois, Joachim Diagnostics (Basel) Article High annotation costs are a substantial bottleneck in applying deep learning architectures to clinically relevant use cases, substantiating the need for algorithms to learn from unlabeled data. In this work, we propose employing self-supervised methods. To that end, we trained with three self-supervised algorithms on a large corpus of unlabeled dental images, which contained 38K bitewing radiographs (BWRs). We then applied the learned neural network representations on tooth-level dental caries classification, for which we utilized labels extracted from electronic health records (EHRs). Finally, a holdout test-set was established, which consisted of 343 BWRs and was annotated by three dental professionals and approved by a senior dentist. This test-set was used to evaluate the fine-tuned caries classification models. Our experimental results demonstrate the obtained gains by pretraining models using self-supervised algorithms. These include improved caries classification performance (6 p.p. increase in sensitivity) and, most importantly, improved label-efficiency. In other words, the resulting models can be fine-tuned using few labels (annotations). Our results show that using as few as 18 annotations can produce ≥45% sensitivity, which is comparable to human-level diagnostic performance. This study shows that self-supervision can provide gains in medical image analysis, particularly when obtaining labels is costly and expensive. MDPI 2022-05-16 /pmc/articles/PMC9140204/ /pubmed/35626392 http://dx.doi.org/10.3390/diagnostics12051237 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Taleb, Aiham Rohrer, Csaba Bergner, Benjamin De Leon, Guilherme Rodrigues, Jonas Almeida Schwendicke, Falk Lippert, Christoph Krois, Joachim Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification |
title | Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification |
title_full | Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification |
title_fullStr | Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification |
title_full_unstemmed | Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification |
title_short | Self-Supervised Learning Methods for Label-Efficient Dental Caries Classification |
title_sort | self-supervised learning methods for label-efficient dental caries classification |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9140204/ https://www.ncbi.nlm.nih.gov/pubmed/35626392 http://dx.doi.org/10.3390/diagnostics12051237 |
work_keys_str_mv | AT talebaiham selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT rohrercsaba selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT bergnerbenjamin selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT deleonguilherme selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT rodriguesjonasalmeida selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT schwendickefalk selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT lippertchristoph selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification AT kroisjoachim selfsupervisedlearningmethodsforlabelefficientdentalcariesclassification |