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A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography
In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007509/ https://www.ncbi.nlm.nih.gov/pubmed/36905074 http://dx.doi.org/10.3390/s23052864 |
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author | Yun, Changhee Eom, Bomi Park, Sungjun Kim, Chanho Kim, Dohwan Jabeen, Farah Kim, Won Hwa Kim, Hye Jung Kim, Jaeil |
author_facet | Yun, Changhee Eom, Bomi Park, Sungjun Kim, Chanho Kim, Dohwan Jabeen, Farah Kim, Won Hwa Kim, Hye Jung Kim, Jaeil |
author_sort | Yun, Changhee |
collection | PubMed |
description | In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator’s experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge. |
format | Online Article Text |
id | pubmed-10007509 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100075092023-03-12 A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography Yun, Changhee Eom, Bomi Park, Sungjun Kim, Chanho Kim, Dohwan Jabeen, Farah Kim, Won Hwa Kim, Hye Jung Kim, Jaeil Sensors (Basel) Article In the medical field, it is delicate to anticipate good performance in using deep learning due to the lack of large-scale training data and class imbalance. In particular, ultrasound, which is a key breast cancer diagnosis method, is delicate to diagnose accurately as the quality and interpretation of images can vary depending on the operator’s experience and proficiency. Therefore, computer-aided diagnosis technology can facilitate diagnosis by visualizing abnormal information such as tumors and masses in ultrasound images. In this study, we implemented deep learning-based anomaly detection methods for breast ultrasound images and validated their effectiveness in detecting abnormal regions. Herein, we specifically compared the sliced-Wasserstein autoencoder with two representative unsupervised learning models autoencoder and variational autoencoder. The anomalous region detection performance is estimated with the normal region labels. Our experimental results showed that the sliced-Wasserstein autoencoder model outperformed the anomaly detection performance of others. However, anomaly detection using the reconstruction-based approach may not be effective because of the occurrence of numerous false-positive values. In the following studies, reducing these false positives becomes an important challenge. MDPI 2023-03-06 /pmc/articles/PMC10007509/ /pubmed/36905074 http://dx.doi.org/10.3390/s23052864 Text en © 2023 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 Yun, Changhee Eom, Bomi Park, Sungjun Kim, Chanho Kim, Dohwan Jabeen, Farah Kim, Won Hwa Kim, Hye Jung Kim, Jaeil A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography |
title | A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography |
title_full | A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography |
title_fullStr | A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography |
title_full_unstemmed | A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography |
title_short | A Study on the Effectiveness of Deep Learning-Based Anomaly Detection Methods for Breast Ultrasonography |
title_sort | study on the effectiveness of deep learning-based anomaly detection methods for breast ultrasonography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007509/ https://www.ncbi.nlm.nih.gov/pubmed/36905074 http://dx.doi.org/10.3390/s23052864 |
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