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Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks
- Source :
- IEEE Transactions on Medical Imaging, 35, 5, pp. 1160-1169, IEEE Transactions on Medical Imaging, 35, 1160-1169
- Publication Year :
- 2016
-
Abstract
- Contains fulltext : 164462.pdf (Publisher’s version ) (Open Access) We propose a novel Computer-Aided Detection (CAD) system for pulmonary nodules using multi-view convolutional networks (ConvNets), for which discriminative features are automatically learnt from the training data. The network is fed with nodule candidates obtained by combining three candidate detectors specifically designed for solid, subsolid, and large nodules. For each candidate, a set of 2-D patches from differently oriented planes is extracted. The proposed architecture comprises multiple streams of 2-D ConvNets, for which the outputs are combined using a dedicated fusion method to get the final classification. Data augmentation and dropout are applied to avoid overfitting. On 888 scans of the publicly available LIDCIDRI dataset, our method reaches high detection sensitivities of 85.4% and 90.1% at 1 and 4 false positives per scan, respectively. An additional evaluation on independent datasets from the ANODE09 challenge and DLCST is performed. We showed that the proposed multi-view ConvNets is highly suited to be used for false positive reduction of a CAD system.
- Subjects :
- Lung Neoplasms
Computer science
Feature extraction
02 engineering and technology
Overfitting
030218 nuclear medicine & medical imaging
Pattern Recognition, Automated
Reduction (complexity)
Machine Learning
Tumours of the digestive tract Radboud Institute for Health Sciences [Radboudumc 14]
03 medical and health sciences
0302 clinical medicine
Discriminative model
0202 electrical engineering, electronic engineering, information engineering
False positive paradox
Humans
Computer vision
Electrical and Electronic Engineering
Dropout (neural networks)
Radiological and Ultrasound Technology
business.industry
Solitary Pulmonary Nodule
Computer Science Applications
Pattern recognition (psychology)
Radiographic Image Interpretation, Computer-Assisted
020201 artificial intelligence & image processing
Artificial intelligence
business
Tomography, X-Ray Computed
Software
Algorithms
Rare cancers Radboud Institute for Health Sciences [Radboudumc 9]
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 35
- Issue :
- 5
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....5427f16cb1350a4be21b0f0d5fd943b1