14 December 2017 Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images
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Abstract
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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Babak Ehteshami Bejnordi, Babak Ehteshami Bejnordi, Guido Zuidhof, Guido Zuidhof, Maschenka Balkenhol, Maschenka Balkenhol, Meyke Hermsen, Meyke Hermsen, Peter Bult, Peter Bult, Bram van Ginneken, Bram van Ginneken, Nico Karssemeijer, Nico Karssemeijer, Geert Litjens, Geert Litjens, Jeroen van der Laak, Jeroen van der Laak, "Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images," Journal of Medical Imaging 4(4), 044504 (14 December 2017). https://doi.org/10.1117/1.JMI.4.4.044504 . Submission: Received: 9 May 2017; Accepted: 14 November 2017
Received: 9 May 2017; Accepted: 14 November 2017; Published: 14 December 2017
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