PN-DetX: A Dedicated Framework for Pulmonary Nodule Detection in X-Ray Images

Abstract

Recent developments in X-ray image based pulmonary nodule detection have achieved remarkable results. However, existing methods are focused on transferring off-the-shelf coarse-grained classification models and fine-grained detection models rather than developing a dedicated framework optimized for nodule detection. In this paper, we propose PN-DetX, which as we know is the first dedicated pulmonary nodule detection framework. PN-DetX incorporates feature fusion and self-attention into X-ray based pulmonary nodule detection tasks, achieving improved detection performance. Specifically, PN-DetX adopts CSPDarknet backbone to extract features, and utilizes feature augmentation module to fuse features from different levels followed by context aggregation module to aggregate semantic information. To evaluate the efficacy of our method, we collect a LArge-scale Pulmonary NOdule Detection dataset, LAPNOD, comprising 2954 X-ray images along with expert-annotated ground truths. Experiments demonstrates that our method outperforms baseline by 3.8 mAP and 5.1 AP50. The dataset and codes will be made in public.