Adaptive Focal Inverse Distance Transform Maps for Cell Recognition

The quantitative analysis of cells is crucial for clinical diagnosis, and effective analysis requires accurate detection and classification. Using point annotations for weakly supervised learning is a common approach for cell recognition, which significantly reduces the labeling workload. Cell recognition methods based on point annotations primarily rely on manually crafted smooth pseudo labels. However, the diversity of cell shapes can render the fixed encodings ineffective. In this paper, we propose a multi-task cell recognition framework. The framework utilizes a regression task to adaptively generate smooth pseudo labels with cell morphological features to guide the robust learning of probability branch and utilizes an additional branch for classification. Meanwhile, in order to address the issue of multiple high-response points in one cell, we introduce Non-Maximum Suppression (NMS) to avoid duplicate detection. On a bone marrow cell recognition dataset, our method is compared with five representative methods. Compared with the best performing method, our method achieves improvements of 2.0 F1 score and 3.6 F1 score in detection and classification, respectively.