Abstract
The purpose of cell counting is to estimate the number of cells in microscopy images. Most popular methods obtain the cell numbers by integrating the density maps that are generated by deep cell counting networks. However, these cell counting networks that reply on estimated cell density maps may leave cell locations in a black-box. In this paper, we propose a novel cell counting network leveraging cell location information to obtain accurate cell numbers. Evaluated on four widely used cell counting datasets, our method which uses cell locations to boost the cell density map generation and cell counting, achieves superior performances compared to the state-of-the-art. The source codes will be available in our Github.
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Acknowledgment
This project was supported by Stony Brook University - Brookhaven National Laboratory (SBU-BNL) seed grant on annotation-efficient deep learning.
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Wang, Z., Yin, Z. (2021). Cell Counting by a Location-Aware Network. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_13
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DOI: https://doi.org/10.1007/978-3-030-87589-3_13
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