3DFPN-HS$^2$: 3D Feature Pyramid Network Based High Sensitivity and Specificity Pulmonary Nodule Detection

TL;DR

This paper introduces a novel 3D feature pyramid network with a high sensitivity and specificity module for pulmonary nodule detection in CT scans, significantly reducing false positives and outperforming existing methods.

Contribution

The paper presents a new 3D feature pyramid network combined with a high sensitivity and specificity module to improve lung nodule detection accuracy and reduce false positives.

Findings

Achieves 90.4% sensitivity at 1/8 false positives per scan.

Outperforms state-of-the-art methods by 15.6%.

Effectively reduces false positives while maintaining high sensitivity.

Abstract

Accurate detection of pulmonary nodules with high sensitivity and specificity is essential for automatic lung cancer diagnosis from CT scans. Although many deep learning-based algorithms make great progress for improving the accuracy of nodule detection, the high false positive rate is still a challenging problem which limited the automatic diagnosis in routine clinical practice. In this paper, we propose a novel pulmonary nodule detection framework based on a 3D Feature Pyramid Network (3DFPN) to improve the sensitivity of nodule detection by employing multi-scale features to increase the resolution of nodules, as well as a parallel top-down path to transit the high-level semantic features to complement low-level general features. Furthermore, a High Sensitivity and Specificity (HS$^2$) network is introduced to eliminate the falsely detected nodule candidates by tracking the appearance changes in continuous CT slices of each nodule candidate. The proposed framework is evaluated on the public Lung Nodule Analysis (LUNA16) challenge dataset. Our method is able to accurately detect lung nodules at high sensitivity and specificity and achieves $90.4\%$ sensitivity with 1/8 false positive per scan which outperforms the state-of-the-art results $15.6\%$.