Cited by 1,086AI in cancer detectionMedical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

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Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

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Yap, Moi Hoon, Pons, Gerard, Martí, Joan, Ganau, Sergi, Sentís, Melcior, Zwiggelaar, Reyer, Davison, Adrian K., Martí, Robert·2018·IEEE Journal of Biomedical and Health Informatics

DOI 10.1109/jbhi.2017.2731873

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Abstract

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.

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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Deep learning
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077

Show all 47 references

Research works printed but not linked: 2

Kupinski, 1999, Computerized detection of mass lesions in digital mammography using radial gradient index filtering, Radiology, 213, 229
Prapavesis, 2003, Breast Ultrasound and US-Guided Interventional Techniques: A Multimedia Teaching File

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Self-citation
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Self-citation
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Authors: Yap, M. H., Pons, G., Martí, J., Ganau, S., Sentís, M., Zwiggelaar, R., Davison, A. K., & Martí, R.
Affiliations: Manchester Metropolitan University (GB)
Universitat Oberta de Catalunya (ES)
Department of Computer Science, University of Girona, Girona, Spain
Corporació Sanitària Parc Taulí (ES)
Radiology Department, UDIAT-Centre Diagnòstic, Corporació Parc Taulí, Sabadell, Spain
Department of Computer Science, Aberystwyth University, Aberystwyth, U.K.
University of Manchester (GB)
ORCID: Yap, Moi Hoon
Martí, Joan
Ganau, Sergi
Davison, Adrian K.
Venue: IEEE Journal of Biomedical and Health Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2018-07
Volume: 22
Issue: 4
Pages: 1218-1226
ISSN: 2168-2194 (ISSN-L)
2168-2208
DOI: 10.1109/jbhi.2017.2731873
OpenAlex: W2744692634
Cited by: 1,086
References: 47
Citations by year: 2026: 133 · 2025: 167 · 2024: 159 · 2023: 159 · 2022: 149 · 2021: 134 · 2020: 97 · 2019: 63 · 2018: 24
FWCI: 29.76
Type: journal article
Language: en
Open access: green
e-space.mmu.ac.uk
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Journal of Biomedical and Health Informatics (publishedVersion)
PubMed (publishedVersion)
The Oberta in Open Access (Universitat Oberta de Catalunya) (publishedVersion)
e-space (Manchester Metropolitan University) (acceptedVersion)
LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion)
Research Explorer (The University of Manchester) (submittedVersion, other-oa)
Funders: Spanish Ministry of Economy and Competitiveness under Project SMARTER ((DPI2015-68442-R))
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords: breast ultrasound, lesion detection, computer-aided diagnosis, convolutional neural networks, U-Net, LeNet, transfer learning, breast cancer, benign versus malignant differentiation, benchmark dataset, deformable part models
MeSH terms: Algorithms, Breast, Breast Neoplasms, Female, Humans, Image Interpretation, Computer-Assisted, Databases, Factual, Ultrasonography, Mammary, Neural Networks, Computer

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Automated Breast Ultrasound Lesions Detection Using Convolutional Neural Networks

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