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.

本文介绍了一种基于点的细胞检测和分类框架，该框架采用了直接回归和分类方法，并采用金字塔式特征聚合策略同步处理底层特征和高层语义。实验结果表明，该方法在在免疫组织化学图像的分析中有着较高的准确性和效率，并具有较高的协助病理学家评估的潜力。