  Cell recognition is a fundamental task in digital histopathology image
analysis. Point-based cell recognition (PCR) methods normally require a vast
number of annotations, which is extremely costly, time-consuming and
labor-intensive. Semi-supervised learning (SSL) can provide a shortcut to make
full use of cell information in gigapixel whole slide images without exhaustive
labeling. However, research into semi-supervised point-based cell recognition
(SSPCR) remains largely overlooked. Previous SSPCR works are all built on
density map-based PCR models, which suffer from unsatisfactory accuracy, slow
inference speed and high sensitivity to hyper-parameters. To address these
issues, end-to-end PCR models are proposed recently. In this paper, we develop
a SSPCR framework suitable for the end-to-end PCR models for the first time.
Overall, we use the current models to generate pseudo labels for unlabeled
images, which are in turn utilized to supervise the models training. Besides,
we introduce a co-teaching strategy to overcome the confirmation bias problem
that generally exists in self-training. A distribution alignment technique is
also incorporated to produce high-quality, unbiased pseudo labels for unlabeled
data. Experimental results on four histopathology datasets concerning different
types of staining styles show the effectiveness and versatility of the proposed
framework. Code is available at
\textcolor{magenta}{\url{https://github.com/windygooo/SSPCR}
