  Histology imaging is an essential diagnosis method to finalize the grade and
stage of cancer of different tissues, especially for breast cancer diagnosis.
Specialists often disagree on the final diagnosis on biopsy tissue due to the
complex morphological variety. Although convolutional neural networks (CNN)
have advantages in extracting discriminative features in image classification,
directly training a CNN on high resolution histology images is computationally
infeasible currently. Besides, inconsistent discriminative features often
distribute over the whole histology image, which incurs challenges in
patch-based CNN classification method. In this paper, we propose a novel
architecture for automatic classification of high resolution histology images.
First, an adapted residual network is employed to explore hierarchical features
without attenuation. Second, we develop a robust deep fusion network to utilize
the spatial relationship between patches and learn to correct the prediction
bias generated from inconsistent discriminative feature distribution. The
proposed method is evaluated using 10-fold cross-validation on 400 high
resolution breast histology images with balanced labels and reports 95%
accuracy on 4-class classification and 98.5% accuracy, 99.6% AUC on 2-class
classification (carcinoma and non-carcinoma), which substantially outperforms
previous methods and close to pathologist performance.
