  Cell detection in histopathology images is of great value in clinical
practice. \textit{Convolutional neural networks} (CNNs) have been applied to
cell detection to improve the detection accuracy, where cell annotations are
required for network training. However, due to the variety and large number of
cells, complete annotations that include every cell of interest in the training
images can be challenging. Usually, incomplete annotations can be achieved,
where positive labeling results are carefully examined to ensure their
reliability but there can be other positive instances, i.e., cells of interest,
that are not included in the annotations. This annotation strategy leads to a
lack of knowledge about true negative samples. Most existing methods simply
treat instances that are not labeled as positive as truly negative during
network training, which can adversely affect the network performance. In this
work, to address the problem of incomplete annotations, we formulate the
training of detection networks as a positive-unlabeled learning problem.
Specifically, the classification loss in network training is revised to take
into account incomplete annotations, where the terms corresponding to negative
samples are approximated with the true positive samples and the other samples
of which the labels are unknown. To evaluate the proposed method, experiments
were performed on a publicly available dataset for mitosis detection in breast
cancer cells, and the experimental results show that our method improves the
performance of cell detection given incomplete annotations for training.
