GSLD: A Global Scanner with Local Discriminator Network for Fast Detection of Sparse Plasma Cell in Immunohistochemistry

Abstract

Compared with abundant application of deep learning on hematoxylin and eosin (H&E) images, the study on immunohistochemical (IHC) images is almost blank, while the diagnosis of chronic endometritis mainly relies on the detection of plasma cells in IHC images. In this paper, a novel framework named Global Scanner with Local Discriminator (GSLD) is proposed to detect plasma cells with highly sparse distribution in IHC whole slide images (WSI) effectively and efficiently. Firstly, input an IHC image, the Global Scanner subnetwork (GSNet) predicts a distribution map, where the candidate plasma cells are localized quickly. Secondly, based on the distribution map, the Local Discriminator subnetwork (LDNet)discriminates true plasma cells by adopting only local information, which greatly speeds up the detection. Moreover, a novel grid-oversampling strategy for WSI preprocessing is proposed to relieve sample imbalance problem. Experimentas show that the proposed framework outperforms the representative object detection networks in both speed and accuracy.