Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images

TL;DR

This paper introduces a multi-task weakly supervised learning framework for cell recognition in immunohistochemical images, addressing challenges of cell variability and limited annotations to improve accuracy in cancer diagnosis.

Contribution

It proposes a novel multi-task learning approach with auxiliary tasks, tissue prior learning, and dynamic masks to enhance cell recognition without extensive labeling.

Findings

Outperforms recent cell recognition methods

Significant improvements shown in ablation studies

Effective in handling cell variability and subtle differences

Abstract

Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal with labor-intensive labeling. However, the inconstant cell morphology and subtle differences between classes also bring challenges. To this end, we present a novel cell recognition framework based on multi-task learning, which utilizes two additional auxiliary tasks to guide robust representation learning of the main task. To deal with misclassification, the tissue prior learning branch is introduced to capture the spatial representation of tumor cells without additional tissue annotation. Moreover, dynamic masks and consistency learning are adopted to learn the invariance of cell scale and shape. We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches. Besides, we have also done some ablation studies to show significant improvements after adding the auxiliary branches.