Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Babak Ehteshami Bejnordi; Guido Zuidhof; Maschenka Balkenhol; Meyke Hermsen; Peter Bult; Bram van Ginneken; Nico Karssemeijer; Geert Litjens; Jeroen van der Laak

arXiv · 1705.03678

Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images

Babak Ehteshami Bejnordi·Guido Zuidhof·Maschenka Balkenhol·Meyke Hermsen·Peter Bult·Bram van Ginneken·Nico Karssemeijer·Geert Litjens·Jeroen van der Laak

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Abstract

Automated classification of histopathological whole-slide images (WSI) of breast tissue requires analysis at very high resolutions with a large contextual area. In this paper, we present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). We first train a CNN using high pixel resolution patches 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 interdependence of tissue structures. Our system is trained and evaluated on a dataset containing 221 WSIs of H&E stained breast tissue specimens. The system achieves an AUC of 0.962 for the binary classification of non-malignant and malignant slides and obtains a three class accuracy of 81.3% for classification of WSIs into normal/benign, DCIS, and IDC, demonstrating its potentials for routine diagnostics.

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Babak Ehteshami Bejnordi, Guido Zuidhof, Maschenka Balkenhol, Meyke Hermsen, Peter Bult, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen van der Laak. 2017-05-10. Context-aware stacked convolutional neural networks for classification of breast carcinomas in whole-slide histopathology images. https://arxiv.org/abs/1705.03678

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