Multi-Class Cell Detection Using Modified Self-Attention
Abstract
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 of 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 uses the standard self-attention method.
Cite
Text
Sugimoto et al. "Multi-Class Cell Detection Using Modified Self-Attention." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022. doi:10.1109/CVPRW56347.2022.00202Markdown
[Sugimoto et al. "Multi-Class Cell Detection Using Modified Self-Attention." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2022.](https://mlanthology.org/cvprw/2022/sugimoto2022cvprw-multiclass/) doi:10.1109/CVPRW56347.2022.00202BibTeX
@inproceedings{sugimoto2022cvprw-multiclass,
title = {{Multi-Class Cell Detection Using Modified Self-Attention}},
author = {Sugimoto, Tatsuhiko and Ito, Hiroaki and Teramoto, Yuki and Yoshizawa, Akihiko and Bise, Ryoma},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2022},
pages = {1854-1862},
doi = {10.1109/CVPRW56347.2022.00202},
url = {https://mlanthology.org/cvprw/2022/sugimoto2022cvprw-multiclass/}
}