Automated pulmonary nodule detection in CT images using deep convolutional neural networks

By Hongtao Xie, Dongbao Yang, Nannan Sun, Zhineng Chen, Yongdong Zhang. Pattern Recognition, 2019

416 citations recorded by the source corpus.

Abstract Lung cancer is one of the leading causes of cancer-related death worldwide. Early diagnosis can effectively reduce the mortality, and computer-aided diagnosis (CAD) as an important way to assist doctors has developed rapidly. In particular, automated pulmonary nodule detection in computed tomography (CT) images is crucial to CAD. It is a challenging task to quickly locate the exact positions of lung nodules. In this paper, a novel automated pulmonary nodule detection framework with 2D convolutional neural network (CNN) is proposed to assist the CT reading process. Firstly, we adjust the structure of Faster R-CNN with two region proposal networks and a deconvolutional layer to detect nodule candidates, and then three models are trained for three kinds of slices for later result fusion. Secondly, a boosting architecture based on 2D CNN is designed for false positive reduction, which is a classifier to distinguish true nodules from the candidates. The misclassified samples are still kept for retraining a model which boosts the sensitivity for pulmonary nodule detection. Finally, the results of these networks are fused to vote out the final classification results. Extensive experiments are conducted on LUNA16, and the sensitivity of nodule candidate detection achieves 86.42%. For the false positive reduction, the sensitivity reaches 73.4% and 74.4% at 1/8 and 1/4 FPs/scan, respectively. It illustrates that the proposed method can obviously achieve accurate pulmonary nodule detection.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7e7b297be6532a16968ac7fcaa0fe96aae238962

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning (2019, background): “An understanding of these trade-offs is critical for sensitive tasks such as hiring [15, 25], health care diagnostics [71, 19], self-driving cars [56], where the introduction of pruning may be at odds with fairness objectives to treat protected attributes uniformly and/or the need to guarantee a…”
Recent advances and clinical applications of deep learning in medical image analysis (2021, background): “Common computer-aided detection (CADe) tasks include detecting lung nodules (Gu et al., 2018; Xie et al., 2019b), breast masses (Akselrod-Ballin et al., 2017; Ribli et al., 2018), lymph nodes (Lin et al., 2019; Zhu et al., 2020b), sclerosis lesions (Nair et al., 2020), etc.”
Randomness In Neural Network Training: Characterizing The Impact of Tooling (2021, background): “Our results suggest that deterministic tooling is critical for ensuring AI safety in sensitive domains such as credit scoring, health care diagnostics [Xie et al., 2019, Gruetzemacher et al., 2018, Badgeley et al., 2019, Oakden-Rayner et al., 2019] and autonomous driving [NHTSA, 2017].”
Characterising Bias in Compressed Models (2020, background): “…compressed deep neural networks in resource constrained but sensitive domains such as hiring (Dastin, 2018; Harwell, 2019), health care diagnostics (Xie et al., 2019; Gruetzemacher et al., 2018; Badgeley et al., 2019; Oakden-Rayner et al., 2019), self-driving cars (NHTSA, 2017) and facial…”
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics (2022, background): “In safety-critical or otherwise sensitive domains such as healthcare diagnostics (Xie et al., 2019; Gruetzemacher et al., 2018; Badgeley et al., 2019; Oakden-Rayner et al., 2019), self-driving cars (NHTSA, 2017), hiring (Dastin, 2018; Harwell, 2019), and many others, providing tools for domain…”