Deformable Proposal-Aware P2PNet: A Universal Network for Cell Recognition under Point Supervision

Point-based cell recognition, which aims to localize and classify cells present in a pathology image, is a fundamental task in digital pathology image analysis. The recently developed point-to-point network (P2PNet) has achieved unprecedented cell recognition accuracy and efficiency compared to methods that rely on intermediate density map representations. However, P2PNet could not leverage multi-scale information since it can only decode a single feature map. Moreover, the distribution of predefined point proposals, which is determined by data properties, restricts the resolution of the feature map to decode, i.e., the encoder design. To lift these limitations, we propose a variant of P2PNet named deformable proposal-aware P2PNet (DPA-P2PNet) in this study. The proposed method uses coordinates of point proposals to directly extract multi-scale region-of-interest (ROI) features for feature enhancement. Such a design also opens up possibilities to exploit dynamic distributions of proposals. We further devise a deformation module to improve the proposal quality. Extensive experiments on four datasets with various staining styles demonstrate that DPA-P2PNet outperforms the state-of-the-art methods on point-based cell recognition, which reveals the high potentiality in assisting pathologist assessments.