  Asthma is a chronic inflammatory disorder of the lower respiratory tract and
naturally occurs in humans and animals including horses. The annotation of an
asthma microscopy whole slide image (WSI) is an extremely labour-intensive task
due to the hundreds of thousands of cells per WSI. To overcome the limitation
of annotating WSI incompletely, we developed a training pipeline which can
train a deep learning-based object detection model with partially annotated
WSIs and compensate class imbalances on the fly. With this approach we can
freely sample from annotated WSIs areas and are not restricted to fully
annotated extracted sub-images of the WSI as with classical approaches. We
evaluated our pipeline in a cross-validation setup with a fixed training set
using a dataset of six equine WSIs of which four are partially annotated and
used for training, and two fully annotated WSI are used for validation and
testing. Our WSI-based training approach outperformed classical sub-image-based
training methods by up to 15\% $mAP$ and yielded human-like performance when
compared to the annotations of ten trained pathologists.
