Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks
By Jia Ding, Aoxue Li, Liwei Wang. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017
365 citations recorded by the source corpus.
Early detection of pulmonary cancer is the most promising way to enhance a patient’s chance for survival. Accurate pulmonary nodule detection in computed tomography (CT) images is a crucial step in diagnosing pulmonary cancer. In this paper, inspired by the successful use of deep convolutional neural networks (DCNNs) in natural image recognition, we propose a novel pulmonary nodule detection approach based on DCNNs. We first introduce a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices. Then, a three-dimensional DCNN is presented for the subsequent false positive reduction. Experimental results of the LUng Nodule Analysis 2016 (LUNA16) Challenge demonstrate the superior detection performance of the proposed approach on nodule detection (average FROC-score of 0.893, ranking the 1st place over all submitted results), which outperforms the best result on the leaderboard of the LUNA16 Challenge (average FROC-score of 0.864).
Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/dabbe2b9310c03999668ee6dbffb9d710fb3a621
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In Pulmonary and Respiratory Medicine
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- FastSurfer - A fast and accurate deep learning based neuroimaging pipeline (2019, neutral): “(Ding et al., 2017; Yu et al., 2018; Ghavami et al., 2018) where a sequence of neighbouring slices instead of a single 2D slice are used for network training.”
- Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “For example, in the task of pulmonary nodule detection, Ding et al. (2017) argued that the original Faster RCNN (Ren et al., 2015) with the VGG-16 network (Liu and Deng, 2015) as its backbone cannot capture representative features of small pulmonary nodules; they introduced a deconvolutional layer…”
- DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification (2018, methodology): “1% FROC using 2D Faster R-CNN followed by extra false positive reduction classifier [5], while we only employ enhanced Faster R-CNN with deep 3D dual path for detection.”