The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
Open accessAbstract
Currently, histopathological tissue examination by a pathologist represents the gold standard for breast lesion diagnostics. Automated classification of histopathological whole-slide images (WSIs) is challenging owing to the wide range of appearances of benign lesions and the visual similarity of ductal carcinoma in-situ (DCIS) to invasive lesions at the cellular level. Consequently, analysis of tissue at high resolutions with a large contextual area is necessary. We present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, DCIS, and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution to capture cellular level information. The feature responses generated by this model are then fed as input to a second CNN, stacked on top of the first. Training of this stacked architecture with large input patches enables learning of fine-grained (cellular) details and global tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of hematoxylin and eosin stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of nonmalignant and malignant slides and obtains a three-class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potential for routine diagnostics.
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Metadata
- Authors
- Bejnordi, B. E., Zuidhof, G., Balkenhol, M., Hermsen, M., Bult, P., van Ginneken, B., Karssemeijer, N., Litjens, G., & van der Laak, J.
- Venue
- Journal of Medical Imaging
- Publisher
- SPIE-Intl Soc Optical Eng
- Published
- 2017-12-14
- Volume
- 4
- Issue
- 04
- Pages
- 1
- ISSN
- 2329-4302 (ISSN-L)
- OpenAlex
- W2613181504
- Cited by
- 185
- References
- 13
- FWCI
- 11.81
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Bejnordi, B. E., Zuidhof, G., Balkenhol, M., Hermsen, M., Bult, P., van Ginneken, B., Karssemeijer, N., Litjens, G., & van der Laak, J. (2017). Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. Journal of Medical Imaging. https://doi.org/10.1117/1.jmi.4.4.044504
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