  Mitotic counting is a vital prognostic marker of tumor proliferation in
breast cancer. Deep learning-based mitotic detection is on par with
pathologists, but it requires large labeled data for training. We propose a
deep classification framework for enhancing mitosis detection by leveraging
class label information, via softmax loss, and spatial distribution information
among samples, via distance metric learning. We also investigate strategies
towards steadily providing informative samples to boost the learning. The
efficacy of the proposed framework is established through evaluation on ICPR
2012 and AMIDA 2013 mitotic data. Our framework significantly improves the
detection with small training data and achieves on par or superior performance
compared to state-of-the-art methods for using the entire training data.
