Automated breast ultrasound lesions detection using convolutional neural networks
Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing...
| Autores: | , , , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/93200 |
| Acceso en línea: | https://hdl.handle.net/10609/93200 |
| Access Level: | acceso embargado |
| Palabra clave: | breast cancer convolutional neural networks lesion detection transfer learning ultrasound imaging cáncer de mama redes neuronales convolucionales detección de lesiones transferencia de aprendizaje imagen de ultrasonido càncer de mama xarxes neuronals convolucionals detecció de lesions transferir l'aprenentatge imatges per ultrasò Breast -- Cancer Mama -- Càncer Mama -- Cáncer |
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Automated breast ultrasound lesions detection using convolutional neural networksYap, Moi HoonPons Rodríguez, GerardMartí Bonmatí, JoanGanau Macías, SergiSentís Crivellé, MelciorZwiggelaar, ReyerDavison, Adrian K.Martí Marly, Robertbreast cancerconvolutional neural networkslesion detectiontransfer learningultrasound imagingcáncer de mamaredes neuronales convolucionalesdetección de lesionestransferencia de aprendizajeimagen de ultrasonidocàncer de mamaxarxes neuronals convolucionalsdetecció de lesionstransferir l'aprenentatgeimatges per ultrasòBreast -- CancerMama -- CàncerMama -- CáncerBreast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure.IEEE Journal of Biomedical and Health InformaticsManchester Metropolitan UniversityUniversitat de GironaAberystwyth UniversityUniversity of ManchesterUniversitat Oberta de Catalunya (UOC)201920192018info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://hdl.handle.net/10609/93200reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésIEEE Journal of Biomedical and Health Informatics, 2018, 22(4)https://doi.org/10.1109/jbhi.2017.2731873implied-oainfo:eu-repo/semantics/embargoedAccessoai:openaccess.uoc.edu:10609/932002026-05-28T12:42:01Z |
| dc.title.none.fl_str_mv |
Automated breast ultrasound lesions detection using convolutional neural networks |
| title |
Automated breast ultrasound lesions detection using convolutional neural networks |
| spellingShingle |
Automated breast ultrasound lesions detection using convolutional neural networks Yap, Moi Hoon breast cancer convolutional neural networks lesion detection transfer learning ultrasound imaging cáncer de mama redes neuronales convolucionales detección de lesiones transferencia de aprendizaje imagen de ultrasonido càncer de mama xarxes neuronals convolucionals detecció de lesions transferir l'aprenentatge imatges per ultrasò Breast -- Cancer Mama -- Càncer Mama -- Cáncer |
| title_short |
Automated breast ultrasound lesions detection using convolutional neural networks |
| title_full |
Automated breast ultrasound lesions detection using convolutional neural networks |
| title_fullStr |
Automated breast ultrasound lesions detection using convolutional neural networks |
| title_full_unstemmed |
Automated breast ultrasound lesions detection using convolutional neural networks |
| title_sort |
Automated breast ultrasound lesions detection using convolutional neural networks |
| dc.creator.none.fl_str_mv |
Yap, Moi Hoon Pons Rodríguez, Gerard Martí Bonmatí, Joan Ganau Macías, Sergi Sentís Crivellé, Melcior Zwiggelaar, Reyer Davison, Adrian K. Martí Marly, Robert |
| author |
Yap, Moi Hoon |
| author_facet |
Yap, Moi Hoon Pons Rodríguez, Gerard Martí Bonmatí, Joan Ganau Macías, Sergi Sentís Crivellé, Melcior Zwiggelaar, Reyer Davison, Adrian K. Martí Marly, Robert |
| author_role |
author |
| author2 |
Pons Rodríguez, Gerard Martí Bonmatí, Joan Ganau Macías, Sergi Sentís Crivellé, Melcior Zwiggelaar, Reyer Davison, Adrian K. Martí Marly, Robert |
| author2_role |
author author author author author author author |
| dc.contributor.none.fl_str_mv |
Manchester Metropolitan University Universitat de Girona Aberystwyth University University of Manchester Universitat Oberta de Catalunya (UOC) |
| dc.subject.none.fl_str_mv |
breast cancer convolutional neural networks lesion detection transfer learning ultrasound imaging cáncer de mama redes neuronales convolucionales detección de lesiones transferencia de aprendizaje imagen de ultrasonido càncer de mama xarxes neuronals convolucionals detecció de lesions transferir l'aprenentatge imatges per ultrasò Breast -- Cancer Mama -- Càncer Mama -- Cáncer |
| topic |
breast cancer convolutional neural networks lesion detection transfer learning ultrasound imaging cáncer de mama redes neuronales convolucionales detección de lesiones transferencia de aprendizaje imagen de ultrasonido càncer de mama xarxes neuronals convolucionals detecció de lesions transferir l'aprenentatge imatges per ultrasò Breast -- Cancer Mama -- Càncer Mama -- Cáncer |
| description |
Breast lesion detection using ultrasound imaging is considered an important step of computer-aided diagnosis systems. Over the past decade, researchers have demonstrated the possibilities to automate the initial lesion detection. However, the lack of a common dataset impedes research when comparing the performance of such algorithms. This paper proposes the use of deep learning approaches for breast ultrasound lesion detection and investigates three different methods: a Patch-based LeNet, a U-Net, and a transfer learning approach with a pretrained FCN-AlexNet. Their performance is compared against four state-of-the-art lesion detection algorithms (i.e., Radial Gradient Index, Multifractal Filtering, Rule-based Region Ranking, and Deformable Part Models). In addition, this paper compares and contrasts two conventional ultrasound image datasets acquired from two different ultrasound systems. Dataset A comprises 306 (60 malignant and 246 benign) images and Dataset B comprises 163 (53 malignant and 110 benign) images. To overcome the lack of public datasets in this domain, Dataset B will be made available for research purposes. The results demonstrate an overall improvement by the deep learning approaches when assessed on both datasets in terms of True Positive Fraction, False Positives per image, and F-measure. |
| publishDate |
2018 |
| dc.date.none.fl_str_mv |
2018 2019 2019 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
| format |
article |
| status_str |
publishedVersion |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/10609/93200 |
| url |
https://hdl.handle.net/10609/93200 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
IEEE Journal of Biomedical and Health Informatics, 2018, 22(4) https://doi.org/10.1109/jbhi.2017.2731873 |
| dc.rights.none.fl_str_mv |
implied-oa info:eu-repo/semantics/embargoedAccess |
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implied-oa |
| eu_rights_str_mv |
embargoedAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.none.fl_str_mv |
IEEE Journal of Biomedical and Health Informatics |
| publisher.none.fl_str_mv |
IEEE Journal of Biomedical and Health Informatics |
| dc.source.none.fl_str_mv |
reponame:O2, repositorio institucional de la UOC instname:Universitat Oberta de Catalunya (UOC) |
| instname_str |
Universitat Oberta de Catalunya (UOC) |
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O2, repositorio institucional de la UOC |
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O2, repositorio institucional de la UOC |
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1869409431961206784 |
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15.301603 |