End-to-end cell recognition by point annotation
This work addresses the challenge of reliable quantitative analysis for pathologists, though it appears incremental as it builds on weakly-supervised methods with improvements in handling dense cell scenarios.
The paper tackles the problem of accurate cell detection and classification in dense immunohistochemical staining images by introducing an end-to-end framework that uses direct regression and classification for preset anchor points, achieving superior accuracy and efficiency in experiments.
Reliable quantitative analysis of immunohistochemical staining images requires accurate and robust cell detection and classification. Recent weakly-supervised methods usually estimate probability density maps for cell recognition. However, in dense cell scenarios, their performance can be limited by pre- and post-processing as it is impossible to find a universal parameter setting. In this paper, we introduce an end-to-end framework that applies direct regression and classification for preset anchor points. Specifically, we propose a pyramidal feature aggregation strategy to combine low-level features and high-level semantics simultaneously, which provides accurate cell recognition for our purely point-based model. In addition, an optimized cost function is designed to adapt our multi-task learning framework by matching ground truth and predicted points. The experimental results demonstrate the superior accuracy and efficiency of the proposed method, which reveals the high potentiality in assisting pathologist assessments.