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Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study
Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of ea...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320909/ https://www.ncbi.nlm.nih.gov/pubmed/34460465 http://dx.doi.org/10.3390/jimaging5030037 |
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author | Tsochatzidis, Lazaros Costaridou, Lena Pratikakis, Ioannis |
author_facet | Tsochatzidis, Lazaros Costaridou, Lena Pratikakis, Ioannis |
author_sort | Tsochatzidis, Lazaros |
collection | PubMed |
description | Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch. |
format | Online Article Text |
id | pubmed-8320909 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83209092021-08-26 Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study Tsochatzidis, Lazaros Costaridou, Lena Pratikakis, Ioannis J Imaging Article Deep convolutional neural networks (CNNs) are investigated in the context of computer-aided diagnosis (CADx) of breast cancer. State-of-the-art CNNs are trained and evaluated on two mammographic datasets, consisting of ROIs depicting benign or malignant mass lesions. The performance evaluation of each examined network is addressed in two training scenarios: the first involves initializing the network with pre-trained weights, while for the second the networks are initialized in a random fashion. Extensive experimental results show the superior performance achieved in the case of fine-tuning a pretrained network compared to training from scratch. MDPI 2019-03-13 /pmc/articles/PMC8320909/ /pubmed/34460465 http://dx.doi.org/10.3390/jimaging5030037 Text en © 2019 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 (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Tsochatzidis, Lazaros Costaridou, Lena Pratikakis, Ioannis Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study |
title | Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study |
title_full | Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study |
title_fullStr | Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study |
title_full_unstemmed | Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study |
title_short | Deep Learning for Breast Cancer Diagnosis from Mammograms—A Comparative Study |
title_sort | deep learning for breast cancer diagnosis from mammograms—a comparative study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8320909/ https://www.ncbi.nlm.nih.gov/pubmed/34460465 http://dx.doi.org/10.3390/jimaging5030037 |
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