The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Classification of breast cancer histology images using Convolutional Neural Networks
Open accessAbstract
Breast cancer is one of the main causes of cancer death worldwide. The diagnosis of biopsy tissue with hematoxylin and eosin stained images is non-trivial and specialists often disagree on the final diagnosis. Computer-aided Diagnosis systems contribute to reduce the cost and increase the efficiency of this process. Conventional classification approaches rely on feature extraction methods designed for a specific problem based on field-knowledge. To overcome the many difficulties of the feature-based approaches, deep learning methods are becoming important alternatives. A method for the classification of hematoxylin and eosin stained breast biopsy images using Convolutional Neural Networks (CNNs) is proposed. Images are classified in four classes, normal tissue, benign lesion, in situ carcinoma and invasive carcinoma, and in two classes, carcinoma and non-carcinoma. The architecture of the network is designed to retrieve information at different scales, including both nuclei and overall tissue organization. This design allows the extension of the proposed system to whole-slide histology images. The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved. The sensitivity of our method for cancer cases is 95.6%.
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Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis
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Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698
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Echle, A., Rindtorff, N. T., Brinker, T. J., Luedde, T., Pearson, A. T., & Kather, J. N. (2021) · British Journal of Cancer · doi:10.1038/s41416-020-01122-x · Cited by 667
Digital pathology and artificial intelligence in translational medicine and clinical practice
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Metadata
- Authors
- Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A.
- Venue
- PLOS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2017-06-01
- Volume
- 12
- Issue
- 6
- Pages
- e0177544
- ISSN
- 1932-6203 (ISSN-L)
- OpenAlex
- W2620578070
- Cited by
- 1,013
- References
- 8
- FWCI
- 59.70
- Type
- journal article
- Language
- en
- Open access
- gold
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- Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017). Classification of breast cancer histology images using Convolutional Neural Networks. PLOS ONE. https://doi.org/10.1371/journal.pone.0177544
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