Accurate Landmark Localization for Medical Images Using Perturbations

Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Junhyeok Kang, Kanghan Oh, Il-Seok Oh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	artificial intelligence landmark localization context feature learning image perturbation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/dcdbf490715e4247b847d7e4ef8de245
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:dcdbf490715e4247b847d7e4ef8de245
record_format	dspace
spelling	oai:doaj.org-article:dcdbf490715e4247b847d7e4ef8de2452021-11-11T15:18:55ZAccurate Landmark Localization for Medical Images Using Perturbations10.3390/app1121102772076-3417https://doaj.org/article/dcdbf490715e4247b847d7e4ef8de2452021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10277https://doaj.org/toc/2076-3417Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medical landmark localization. To this end, we report an extensive experiment that uses the perturbation methods of erasing, smoothing, binarization, and edge detection. The hand X-ray dataset and the ISBI 2015 Cephalometric dataset are used to evaluate the perturbation effect. The experimental results show that the perturbation method forces the network to extract richer representations of an image, leading to performance increases. Moreover, in comparison with the existing methods that lack any complex algorithmic change of network, our methods with specific perturbation methods achieve superior performance.Junhyeok KangKanghan OhIl-Seok OhMDPI AGarticleartificial intelligencelandmark localizationcontext feature learningimage perturbationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10277, p 10277 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence landmark localization context feature learning image perturbation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	artificial intelligence landmark localization context feature learning image perturbation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Junhyeok Kang Kanghan Oh Il-Seok Oh Accurate Landmark Localization for Medical Images Using Perturbations
description	Recently, various studies have been proposed to learn the rich representations of images during deep learning. In particular, the perturbation method is a simple way to learn rich representations that has shown significant success. In this study, we present effective perturbation approaches for medical landmark localization. To this end, we report an extensive experiment that uses the perturbation methods of erasing, smoothing, binarization, and edge detection. The hand X-ray dataset and the ISBI 2015 Cephalometric dataset are used to evaluate the perturbation effect. The experimental results show that the perturbation method forces the network to extract richer representations of an image, leading to performance increases. Moreover, in comparison with the existing methods that lack any complex algorithmic change of network, our methods with specific perturbation methods achieve superior performance.
format	article
author	Junhyeok Kang Kanghan Oh Il-Seok Oh
author_facet	Junhyeok Kang Kanghan Oh Il-Seok Oh
author_sort	Junhyeok Kang
title	Accurate Landmark Localization for Medical Images Using Perturbations
title_short	Accurate Landmark Localization for Medical Images Using Perturbations
title_full	Accurate Landmark Localization for Medical Images Using Perturbations
title_fullStr	Accurate Landmark Localization for Medical Images Using Perturbations
title_full_unstemmed	Accurate Landmark Localization for Medical Images Using Perturbations
title_sort	accurate landmark localization for medical images using perturbations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dcdbf490715e4247b847d7e4ef8de245
work_keys_str_mv	AT junhyeokkang accuratelandmarklocalizationformedicalimagesusingperturbations AT kanghanoh accuratelandmarklocalizationformedicalimagesusingperturbations AT ilseokoh accuratelandmarklocalizationformedicalimagesusingperturbations
_version_	1718435398278971392

Accurate Landmark Localization for Medical Images Using Perturbations

Ejemplares similares