Multi-Class Cell Detection Using Modified Self-Attention

Tatsuhiko Sugimoto, Hiroaki Ito, Yuki Teramoto, Akihiko Yoshizawa, Ryoma Bise; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2022, pp. 1855-1863

Multi-class cell detection (cancer or non-cancer) from a whole slide image (WSI) is an important task for pathological diagnosis. Cancer and non-cancer cells often have a similar appearance, so it is difficult even for experts to classify a cell from a patch image of individual cells. They usually identify the cell type not only on the basis of the appearance of a single cell but also on the context from the surrounding cells. For using such information, we propose a multi-class cell-detection method that introduces a modified self-attention to aggregate the surrounding image features of both classes. Experimental results demonstrate the effectiveness of the proposed method; our method achieved the best performance compared with a method, which simply use the standard self-attention method.