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Ki67 is an important biomarker for breast cancer. Classification of positiveand negative Ki67 cells in histology slides is a common approach to determinecancer proliferation status. However, there is a lack of generalizable andaccurate methods to automate Ki67 scoring in large-scale patient cohorts. Inthis work, we have employed a novel deep learning technique based onhypercolumn 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 forthe training and fine tuning to training data. We extracted the hypercolumndescriptors of each cell to form the vector of activation from specific layersto capture features at different granularity. Features from these layers thatcorrespond to the same pixel were propagated using a stochastic gradientdescent optimizer to yield the detection of the nuclei and the final cellsegmentations. Subsequently, seeds generated from cell segmentation werepropagated to a spatially constrained convolutional neural network for theclassification of the cells into stromal, lymphocyte, Ki67-positive cancercell, and Ki67-negative cancer cell. We validated its accuracy in the contextof 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 Ki67stained breast cancer dataset comprising biopsy and whole-slide images.

DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images