  Ki67 is an important biomarker for breast cancer. Classification of positive
and negative Ki67 cells in histology slides is a common approach to determine
cancer proliferation status. However, there is a lack of generalizable and
accurate methods to automate Ki67 scoring in large-scale patient cohorts. In
this work, we have employed a novel deep learning technique based on
hypercolumn descriptors for cell classification in Ki67 images. Specifically,
we developed the Simultaneous Detection and Cell Segmentation (DeepSDCS)
network to perform cell segmentation and detection. VGG16 network was used for
the training and fine tuning to training data. We extracted the hypercolumn
descriptors of each cell to form the vector of activation from specific layers
to capture features at different granularity. Features from these layers that
correspond to the same pixel were propagated using a stochastic gradient
descent optimizer to yield the detection of the nuclei and the final cell
segmentations. Subsequently, seeds generated from cell segmentation were
propagated to a spatially constrained convolutional neural network for the
classification of the cells into stromal, lymphocyte, Ki67-positive cancer
cell, and Ki67-negative cancer cell. We validated its accuracy in the context
of a large-scale clinical trial of oestrogen-receptor-positive breast cancer.
We achieved 99.06% and 89.59% accuracy on two separate test sets of Ki67
stained breast cancer dataset comprising biopsy and whole-slide images.
