  Deep learning has achieved unprecedented success in various object detection
tasks with huge amounts of labeled data. However, obtaining large-scale
annotations for medical images is extremely challenging due to the high demand
of labour and expertise. In this paper, we propose a novel deep semi-supervised
metric learning method to effectively leverage both labeled and unlabeled data
for cervical cancer cell detection. Specifically, our model learns a metric
space and conducts dual alignment of semantic features on both the proposal
level and the prototype levels. On the proposal level, we align the unlabeled
data with class proxies derived from the labeled data. We further align the
prototypes of the labeled and unlabeled data to alleviate the influence of
possibly noisy pseudo labels generated at the proposal alignment stage.
Moreover, we adopt a memory bank to store the labeled prototypes, which
significantly enrich the metric learning information from larger batches.
Extensive experiments show our proposed method outperforms other
state-of-the-art semi-supervised approaches consistently, demonstrating the
efficacy of our proposed deep semi-supervised metric learning with dual
alignment.
