  With availability of huge amounts of labeled data, deep learning has achieved
unprecedented success in various object detection tasks. However, large-scale
annotations for medical images are extremely challenging to be acquired due to
the high demand of labour and expertise. To address this difficult issue, in
this paper we propose a novel semi-supervised deep metric learning method to
effectively leverage both labeled and unlabeled data with application to
cervical cancer cell detection. Different from previous methods, our model
learns an embedding metric space and conducts dual alignment of semantic
features on both the proposal and prototype levels. First, on the proposal
level, we generate pseudo labels for the unlabeled data to align the proposal
features with learnable class proxies derived from the labeled data.
Furthermore, we align the prototypes generated from each mini-batch of labeled
and unlabeled data to alleviate the influence of possibly noisy pseudo labels.
Moreover, we adopt a memory bank to store the labeled prototypes and hence
significantly enrich the metric learning information from larger batches. To
comprehensively validate the method, we construct a large-scale dataset for
semi-supervised cervical cancer cell detection for the first time, consisting
of 240,860 cervical cell images in total. Extensive experiments show our
proposed method outperforms other state-of-the-art semi-supervised approaches
consistently, demonstrating efficacy of deep semi-supervised metric learning
with dual alignment on improving cervical cancer cell detection performance.
