We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Enhancement of the Individual Selectness Using Local Spatial Weighting for Immune Cells.
- Authors
Kusunose, Shoya; Shinomiya, Yuki; Ushiwaka, Takashi; Maeda, Nagamasa; Hoshino, Yukinobu
- Abstract
This paper focuses on the analysis of the activity of immune cells for supporting medical workers. Recognition frequency space selects a region including neighboring multiple cells as a single cell is one of the major issues in activity analysis of immune cells. This study focuses on the locality of immune cell features and uses a high-velocity weighting method for the analysis while the Gaussian distribution is used in the literature. The analysis was conducted for a few well-known methods such as final feature maps, class activation mapping (CAM), gradient weighted class activation mapping (Grad-CAM), Grad-CAM++, and Eigen-CAM. The results show that the densely inhabited immune cells are correctly selected by CAM, Grad-CAM, Grad-CAM++, and Eigen-CAM. These algorithms also show stability with respect to the threshold used to select tracking targets. In addition, the higher threshold makes the selection robust, and the lower one is useful for analyzing tends of multiple cells in a whole frame efficiently.
- Subjects
ARTIFICIAL neural networks; IMAGE processing; GAUSSIAN distribution; VISUALIZATION; ROBUST control
- Publication
Journal of Advanced Computational Intelligence & Intelligent Informatics, 2022, Vol 26, Issue 2, p178
- ISSN
1343-0130
- Publication type
Article
- DOI
10.20965/jaciii.2022.p0178