Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression

@inproceedings{He2019AutomaticMC,
  title={Automatic microscopic cell counting by use of unsupervised adversarial domain adaptation and supervised density regression},
  author={Shenghua He and Kyaw Thu Minn and L. Solnica-Krezel and H. Li and M. Anastasio},
  booktitle={Medical Imaging},
  year={2019}
}
Accurate cell counting in microscopic images is important for medical diagnoses and biological studies. However, manual cell counting is very time-consuming, tedious, and prone to subjective errors. We propose a new density regression-based method for automatic cell counting that reduces the need to manually annotate experimental images. A supervised learning-based density regression model (DRM) is trained with annotated synthetic images (the source domain) and their corresponding ground truth… Expand
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