• Corpus ID: 44078131

RUN: Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection

@article{Lan2018RUNRU,
  title={RUN: Residual U-Net for Computer-Aided Detection of Pulmonary Nodules without Candidate Selection},
  author={Tian Lan and Yuanyuan Li and Jonah Kimani Murugi and Yi Ding and Zhiguang Qin},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.11856}
}
The early detection and early diagnosis of lung cancer are crucial to improve the survival rate of lung cancer patients. Pulmonary nodules detection results have a significant impact on the later diagnosis. In this work, we propose a new network named RUN to complete nodule detection in a single step by bypassing the candidate selection. The system introduces the shortcut of the residual network to improve the traditional U-Net, thereby solving the disadvantage of poor results due to its lack… 
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Methods Citations
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References

SHOWING 1-10 OF 33 REFERENCES

Automated Pulmonary Nodule Detection via 3D ConvNets with Online Sample Filtering and Hybrid-Loss Residual Learning

A novel framework with 3D convolutional networks (ConvNets) for automated detection of pulmonary nodules from low-dose CT scans, which is a challenging yet crucial task for lung cancer early diagnosis and treatment is proposed.

Multi-level Contextual 3D CNNs for False Positive Reduction in Pulmonary Nodule Detection

  • Computer Science
  • 2017
A novel method employing three-dimensional convolutional neural networks for false positive reduction in automated pulmonary nodule detection from volumetric computed tomography (CT) scans and a simple yet effective strategy to encode multilevel contextual information to meet the challenges coming with the large variations and hard mimics of pulmonary nodules.

Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection

A novel method employing three-dimensional convolutional neural networks for false positive reduction in automated pulmonary nodule detection from volumetric computed tomography (CT) scans and a simple yet effective strategy to encode multilevel contextual information to meet the challenges coming with the large variations and hard mimics of pulmonary nodules.

Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks

This paper introduces a deconvolutional structure to Faster Region-based Convolutional Neural Network (Faster R-CNN) for candidate detection on axial slices and proposes a novel pulmonary nodule detection approach based on DCNNs.

Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks

It was showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.

Lung nodule detection in CT images using deep convolutional neural networks

This CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe systems more challenging.

Multi-scale Convolutional Neural Networks for Lung Nodule Classification

A hierarchical learning framework--Multi-scale Convolutional Neural Networks (MCNN)-- is proposed to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers to sufficiently quantify nodule characteristics.

Lung nodule detection in CT using 3D convolutional neural networks

The experimental results showed benefits of using a priori models to reduce the problem space for data-driven machine learning of complex deep neural networks and the advantages of 3D CNN over 2D CNN in volumetric medical image analysis.

A novel approach to CAD system for the detection of lung nodules in CT images

Pulmonary nodule detection using a cascaded SVM classifier

This paper addresses the problem of lung nodule detection via a cascaded SVM classifier by sequentially performing two classification tasks in order to select from an extremely large pool of potential candidates the few most likely ones.