Weakly Supervised Learning for cell recognition in immunohistochemical cytoplasm staining images
This work addresses the problem of labor-intensive labeling in cancer diagnosis for pathologists, but it appears incremental as it builds on existing weakly supervised methods with specific enhancements.
The paper tackles cell recognition in immunohistochemical cytoplasm staining images for cancer diagnosis by proposing a weakly supervised learning framework based on multi-task learning with auxiliary tasks, and it reports that the method outperforms recent approaches.
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.