S4ND: Single-Shot Single-Scale Lung Nodule Detection
This work addresses the problem of computationally expensive nodule detection for medical imaging practitioners, offering a more efficient and accurate solution, though it is incremental as it builds on existing deep learning frameworks.
The authors tackled the computational inefficiency of multi-stage lung nodule detection in CT scans by proposing S4ND, a single-shot, single-scale 3D CNN that achieved an average FROC-score of 0.897 on the LUNA dataset, outperforming existing methods in both accuracy and efficiency.
The state of the art lung nodule detection studies rely on computationally expensive multi-stage frameworks to detect nodules from CT scans. To address this computational challenge and provide better performance, in this paper we propose S4ND, a new deep learning based method for lung nodule detection. Our approach uses a single feed forward pass of a single network for detection and provides better performance when compared to the current literature. The whole detection pipeline is designed as a single $3D$ Convolutional Neural Network (CNN) with dense connections, trained in an end-to-end manner. S4ND does not require any further post-processing or user guidance to refine detection results. Experimentally, we compared our network with the current state-of-the-art object detection network (SSD) in computer vision as well as the state-of-the-art published method for lung nodule detection (3D DCNN). We used publically available $888$ CT scans from LUNA challenge dataset and showed that the proposed method outperforms the current literature both in terms of efficiency and accuracy by achieving an average FROC-score of $0.897$. We also provide an in-depth analysis of our proposed network to shed light on the unclear paradigms of tiny object detection.