  Cell nuclei detection and fine-grained classification have been fundamental
yet challenging problems in histopathology image analysis. Due to the nuclei
tiny size, significant inter-/intra-class variances, as well as the inferior
image quality, previous automated methods would easily suffer from limited
accuracy and robustness. In the meanwhile, existing approaches usually deal
with these two tasks independently, which would neglect the close relatedness
of them. In this paper, we present a novel method of sibling fully
convolutional network with prior objectness interaction (called SFCN-OPI) to
tackle the two tasks simultaneously and interactively using a unified
end-to-end framework. Specifically, the sibling FCN branches share features in
earlier layers while holding respective higher layers for specific tasks. More
importantly, the detection branch outputs the objectness prior which
dynamically interacts with the fine-grained classification sibling branch
during the training and testing processes. With this mechanism, the
fine-grained classification successfully focuses on regions with high
confidence of nuclei existence and outputs the conditional probability, which
in turn benefits the detection through back propagation. Extensive experiments
on colon cancer histology images have validated the effectiveness of our
proposed SFCN-OPI and our method has outperformed the state-of-the-art methods
by a large margin.
