  In digital pathology, cell detection and classification are often
prerequisites to quantify cell abundance and explore tissue spatial
heterogeneity. However, these tasks are particularly challenging for multiplex
immunohistochemistry (mIHC) images due to high levels of variability in
staining, expression intensity, and inherent noise as a result of preprocessing
artefacts. We proposed a deep learning method to detect and classify cells in
mIHC whole-tumour slide images of breast cancer. Inspired by inception-v3, we
developed Cell COunt RegularizeD Convolutional neural Network (ConCORDe-Net)
which integrates conventional dice overlap and a new cell count loss function
for optimizing cell detection, followed by a multi-stage convolutional neural
network for cell classification. In total, 20447 cells, belonging to five cell
classes were annotated by experts from 175 patches extracted from 6
whole-tumour mIHC images. These patches were randomly split into training,
validation and testing sets. Using ConCORDe-Net, we obtained a cell detection
F1 score of 0.873, which is the best score compared to three state of the art
methods. In particular, ConCORDe-Net excels at detecting closely located and
weakly stained cells compared to other methods. Incorporating cell count loss
in the objective function regularizes the network to learn weak gradient
boundaries and separate weakly stained cells from background artefacts.
Moreover, cell classification accuracy of 96.5% was achieved. These results
support that incorporating problem-specific knowledge such as cell count into
deep learning-based cell detection architectures improve the robustness of the
algorithm.
