@inproceedings{1620d8541b6c48ca8a9544ee5efaa72e,
title = "Cell Counting by a Location-Aware Network",
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.",
keywords = "Cell counting, Set loss, Supervised learning",
author = "Zuhui Wang and Zhaozheng Yin",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 12th International Workshop on Machine Learning in Medical Imaging, MLMI 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 ; Conference date: 27-09-2021 Through 27-09-2021",
year = "2021",
doi = "10.1007/978-3-030-87589-3\_13",
language = "English",
isbn = "9783030875886",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "120--129",
editor = "Chunfeng Lian and Xiaohuan Cao and Islem Rekik and Xuanang Xu and Pingkun Yan",
booktitle = "Machine Learning in Medical Imaging - 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Proceedings",
}