Improving the Preciseness of Single Selection of Neighboring Cells in Immune Cell Selection using Partial Weighting

Abstract

There has been a lot of research in the medical field aimed at substituting artificial intelligence technology for diagnosis and analysis by medical professionals. Among them, we focused on the analysis of immune cells. Immune cells have been analyzed manually, and cell selection in particular has been a heavy burden for workers. Although Recognition Frequency Space has been proposed to automate the selection process, it is difficult to obtain the neighboring cells individually. In this paper, we propose a selection method to improve the selectivity of neighboring cells using Grad-CAM and Grad-CAM++. As a result of comparison with the previous methods, the selection results using Grad-CAM showed that each of the three neighboring cells could be selected individually. This is because of the higher performance of Grad-CAM in performing precisely weighting compared to previous methods.