The DOI matches its Crossref record; 70 of the 71 references it cites were found in a registry or an index.
A deep learning framework for supporting the classification of breast lesions in ultrasound images
Open accessAbstract
In this research, we exploited the deep learning framework to differentiate the distinctive types of lesions and nodules in breast acquired with ultrasound imaging. A biopsy-proven benchmarking dataset was built from 5151 patients cases containing a total of 7408 ultrasound breast images, representative of semi-automatically segmented lesions associated with masses. The dataset comprised 4254 benign and 3154 malignant lesions. The developed method includes histogram equalization, image cropping and margin augmentation. The GoogLeNet convolutionary neural network was trained to the database to differentiate benign and malignant tumors. The networks were trained on the data with augmentation and the data without augmentation. Both of them showed an area under the curve of over 0.9. The networks showed an accuracy of about 0.9 (90%), a sensitivity of 0.86 and a specificity of 0.96. Although target regions of interest (ROIs) were selected by radiologists, meaning that radiologists still have to point out the location of the ROI, the classification of malignant lesions showed promising results. If this method is used by radiologists in clinical situations it can classify malignant lesions in a short time and support the diagnosis of radiologists in discriminating malignant lesions. Therefore, the proposed method can work in tandem with human radiologists to improve performance, which is a fundamental purpose of computer-aided diagnosis.
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“In addition, the stacked denoising auto-encoder (SDAE) [88], a combination of the point-wise gated Boltzmann machine (PGBM) and RBM [89], and the GoogLeNet CNN [90] have been applied to breast US or shear-wave elastography images for breast cancer diagnosis; all of these obtained a superior performance when compared with human experts.”
From Deep Learning in Medical Ultrasound Analysis: A Review (2019) · cites this paper as [90]
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
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
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
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
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
Contour Detection and Hierarchical Image Segmentation
Arbeláez, P., Maire, M., Fowlkes, C. C., & Malik, J. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.161 · Cited by 5,581
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Navab, N., Joachim Hornegger, A., Wells, W. M., & Frangi, A. F. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24553-9 · Cited by 2,288
Computer-aided diagnosis in medical imaging: historical review, current status and future potential
Doi, K. (2007) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2007.02.002 · Cited by 1,855
Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
Roche, A., Malandain, G., Pennec, X., & Ayache, N. (1998) · Lecture notes in computer science · doi:10.1007/bfb0056181 · Cited by 1,578
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Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
Convolutional Neural Networks for Radiologic Images: A Radiologist’s Guide
Soffer, S., Ben-Cohen, A., Shimon, O., Amitai, M. M., Greenspan, H. K., & Klang, E. (2019) · Radiology · doi:10.1148/radiol.2018180547 · Cited by 528
Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review
Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114161 · Cited by 429
Deep Learning for Medical Anomaly Detection – A Survey
Fernando, T., Gammulle, H., Denman, S., Sridharan, S., & Fookes, C. (2021) · ACM Computing Surveys · doi:10.1145/3464423 · Cited by 449
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Byra, M., Jarosik, P., Szubert, A., Galperin, M. Y., Ojeda‐Fournier, H., Olson, L. K., O'Boyle, M. K., Comstock, C., & André, M. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102027 · Cited by 328
Metadata
- Authors
- Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K.
- Affiliations
- Korea National University of Transportation (KR)
- Samsung Electronics (South Korea) (KR)
- Corresponding author
- Seong, Yeong-Kyeong
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2017-09-15
- Volume
- 62
- Issue
- 19
- Pages
- 7714-7728
- ISSN
- 0031-9155 (ISSN-L)
- 1361-6560
- OpenAlex
- W2740028789
- Cited by
- 358
- References
- 71
- Citations by year
- 2026: 13 · 2025: 31 · 2024: 48 · 2023: 51 · 2022: 55 · 2021: 61 · 2020: 52 · 2019: 34 · 2018: 13
- FWCI
- 16.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Physics in Medicine and Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- breast ultrasound, breast lesion classification, benign versus malignant tumors, computer-aided diagnosis, GoogLeNet, convolutional neural networks, image augmentation
- MeSH terms
- Machine Learning, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Image Interpretation, Computer-Assisted, Middle Aged, ROC Curve, Ultrasonography, Mammary, Neural Networks, Computer
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