Cascaded multi-point regression Network for high-quality generic lesion detection

Abstract

With such a high incidence of cancer, research on high-quality lesion detection is extremely significant to improve diagnostic efficiency. With the ability to correctly recognize the lesion, it is even more significant to accurately locate the lesion, which can contribute effectively to the subsequent segmentation and radiation of the lesion. In this paper, we propose a cascaded multi-point regression detection network, which can predict offsets of multiple local points of proposals. Different from traditional regression and location based on keypoints, Multi-point regression (MPR) predicts multi offsets for local points that are position sensitive instead of regressing the whole object proposal. In order to reduce the influence of background region on the final box regression, we set the corresponding regression weight for each local point according to different IoU thresholds. At low IoU threshold, the category score is used as the regression weight for each point. At high IoU threshold, a binary weight prediction is proposed to filter out the background area. For improving the transmission and extraction of the underlying geometric location information, we build a bottom-up information propagation path in FPN as an augmented feature pyramid network (AFPN). We use the dataset DeepLesion to evaluate our network, and the experimental results show that our method can perform as expected. The detection network can improve the quality of lesion detection in terms of localization accuracy compared to most mainstream models.