DPD-Net: Dual-path Proposal Discriminative Network for abnormal cell detection in cervical cytology images.

Cytology inspection is a basic examination for the prevention of cervical cancer, which is still done manually and is a labor-intensive and time-consuming process with high inter-observer variability. With the rapid development of deep learning, automatic cytology inspection methods have achieved gratifying results, but the research on the effective detection of abnormal cells remains insufficient. In this paper, we propose a novel Dual-path Proposal Discriminative detection Network (DPD-Net) for abnormal cell detection in cervical cytology images. Specifically, considering the distinctive characteristics of abnormal nuclei such as increased sizes and unclear boundaries, we first design a dual-path architecture, where the cell path acts as the primary detector and the nucleus path serves as the auxiliary detector to provide supplementary information. In addition, the proposal information isolation may result in ambiguous classification boundaries. To transcend individual features and delve deeper into cellular relationships as implicit features, the proposal relation modules are added to explore relation information and refine features adaptively. Finally, to tackle problems of intra-class variation and inter-class similarity, we calculate the proposal contrastive losses in two paths respectively to guide a better distinction between the features of normal and abnormal cells. The proposed method is evaluated on our in-house cervical liquid-based cytology dataset (CLBC) and a public pap smear cytology dataset (CRIC) and gains satisfactory results, outperforming other state-of-the-art methods. Comprehensive experiments demonstrate the superiority of the proposed DPD-Net and the effectiveness of each proposed component. Our code is available at https://github.com/chaisiyii/DPD-Net. • We design a dual-path network (DP) to parallelly detect abnormal cells and nuclei. • We add proposal relation modules (PRM) to two paths to refine features adaptively. • We add the proposal contrastive loss (PCL) to lead a discriminative feature learning. • We conduct intensive experiments on two cervical cytology datasets: CLBC, CRIC. [Display omitted] [ABSTRACT FROM AUTHOR]