  Breast cancer diagnosis often requires accurate detection of metastasis in
lymph nodes through Whole-slide Images (WSIs). Recent advances in deep
convolutional neural networks (CNNs) have shown significant successes in
medical image analysis and particularly in computational histopathology.
Because of the outrageous large size of WSIs, most of the methods divide one
slide into lots of small image patches and perform classification on each patch
independently. However, neighboring patches often share spatial correlations,
and ignoring these spatial correlations may result in inconsistent predictions.
In this paper, we propose a neural conditional random field (NCRF) deep
learning framework to detect cancer metastasis in WSIs. NCRF considers the
spatial correlations between neighboring patches through a fully connected CRF
which is directly incorporated on top of a CNN feature extractor. The whole
deep network can be trained end-to-end with standard back-propagation algorithm
with minor computational overhead from the CRF component. The CNN feature
extractor can also benefit from considering spatial correlations via the CRF
component. Compared to the baseline method without considering spatial
correlations, we show that the proposed NCRF framework obtains probability maps
of patch predictions with better visual quality. We also demonstrate that our
method outperforms the baseline in cancer metastasis detection on the
Camelyon16 dataset and achieves an average FROC score of 0.8096 on the test
set. NCRF is open sourced at https://github.com/baidu-research/NCRF.
