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...
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Format: | Article |
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MDPI AG
2021-11-01
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Series: | Applied Sciences |
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Online Access: | https://www.mdpi.com/2076-3417/11/21/10277 |
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author | Junhyeok Kang Kanghan Oh Il-Seok Oh |
author_facet | Junhyeok Kang Kanghan Oh Il-Seok Oh |
author_sort | Junhyeok Kang |
collection | DOAJ |
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. |
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format | Article |
id | doaj.art-dcdbf490715e4247b847d7e4ef8de245 |
institution | Directory Open Access Journal |
issn | 2076-3417 |
language | English |
last_indexed | 2024-03-10T06:06:58Z |
publishDate | 2021-11-01 |
publisher | MDPI AG |
record_format | Article |
series | Applied Sciences |
spelling | doaj.art-dcdbf490715e4247b847d7e4ef8de2452023-11-22T20:30:26ZengMDPI AGApplied Sciences2076-34172021-11-0111211027710.3390/app112110277Accurate Landmark Localization for Medical Images Using PerturbationsJunhyeok Kang0Kanghan Oh1Il-Seok Oh2Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, KoreaDepartment of Computer and Software Engineering, Wonkwang University, Iksan 54538, KoreaDivision of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, KoreaRecently, 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.https://www.mdpi.com/2076-3417/11/21/10277artificial intelligencelandmark localizationcontext feature learningimage perturbation |
spellingShingle | Junhyeok Kang Kanghan Oh Il-Seok Oh Accurate Landmark Localization for Medical Images Using Perturbations Applied Sciences artificial intelligence landmark localization context feature learning image perturbation |
title | 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_short | Accurate Landmark Localization for Medical Images Using Perturbations |
title_sort | accurate landmark localization for medical images using perturbations |
topic | artificial intelligence landmark localization context feature learning image perturbation |
url | https://www.mdpi.com/2076-3417/11/21/10277 |
work_keys_str_mv | AT junhyeokkang accuratelandmarklocalizationformedicalimagesusingperturbations AT kanghanoh accuratelandmarklocalizationformedicalimagesusingperturbations AT ilseokoh accuratelandmarklocalizationformedicalimagesusingperturbations |