DAU-Net: A Regression Cell Counting Method
Konferenz: ISCTT 2021 - 6th International Conference on Information Science, Computer Technology and Transportation
26.11.2021 - 28.11.2021 in Xishuangbanna, China
Tagungsband: ISCTT 2021
Seiten: 6Sprache: EnglischTyp: PDFPersönliche VDE-Mitglieder erhalten auf diesen Artikel 10% Rabatt
Zhu, Yiming; Tang, Songyuan; Jiang, Yurong; Kang, Ruirui (Laboratory of Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China)
Image-based cell counting is a challenging task and has a wide range of clinical applications such as biomedical diagnosis and pathological analysis. In this paper, we proposed a new deep learning network structure for cell counting based on regression. First, to overcome uneven and overlap distribution of cells, we designed a dual attention U-Net (DAU-Net), which combines U-Net with spatial and channel attention to provide rich global information. Second, we designed an instance-batch normalization method to alleviate the generalization error by data augmentation, so that our model can achieve good results on data sets with different volumes. We evaluated our method on three public benchmark datasets: synthetic fluorescence microscopy dataset, human subcutaneous adipose tissue dataset, and Dublin cell counting dataset. Results showed that our method achieved satisfactory results on these three datasets.