  Automated analysis of tissue sections allows a better understanding of
disease biology and may reveal biomarkers that could guide prognosis or
treatment selection. In digital pathology, less abundant cell types can be of
biological significance, but their scarcity can result in biased and
sub-optimal cell detection model. To minimize the effect of cell imbalance on
cell detection, we proposed a deep learning pipeline that considers the
abundance of cell types during model training. Cell weight images were
generated, which assign larger weights to less abundant cells and used the
weights to regularize Dice overlap loss function. The model was trained and
evaluated on myeloma bone marrow trephine samples. Our model obtained a cell
detection F1-score of 0.78, a 2% increase compared to baseline models, and it
outperformed baseline models at detecting rare cell types. We found that
scaling deep learning loss function by the abundance of cells improves cell
detection performance. Our results demonstrate the importance of incorporating
domain knowledge on deep learning methods for pathological data with class
imbalance.

Image and Video Processing;Computer Vision and Pattern Recognition