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

By Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Geert J. S. Litjens, P. K. Gerke, C. Jacobs, S. Riel, M. Wille, Matiullah Naqibullah, Clara I. Sánchez, B. van Ginneken. IEEE Transactions on Medical Imaging, 2016

1,170 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/06c8b4c1dd0c045bc6a08d0062c5042b5588d55b

How researchers see this paper

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

In Pulmonary and Respiratory Medicine

1. B. van Ginneken
2. Ronald M. Summers
3. Xiaosong Wang
4. Qi-Yan Dou
5. P. Heng
6. Lequan Yu
7. Hao Chen
8. Le Lu

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. Ronald M. Summers
3. P. Heng
4. Hao Chen
5. Lequan Yu
6. Xiaosong Wang
7. Jing Qin
8. Qi-Yan Dou

Citations in the corpus

• ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases (2017, background): “llection of important medical applications on lymph node and interstitial lung disease detection and classification [37,43]; cerebral microbleed detection [11]; pulmonary nodule detection in CT images [40]; automated pancreas segmentation [36]; cell image segmentation and tracking [35], predicting sp”
• Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas (2018, background): “24-26 Recent use of CNNs has started to yield promising results in multiple medical imaging disciplines, including the detection of pulmonary nodules, 27 colon cancer, 28 and cerebral microbleeds.”
• Arti fi cial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success (background): “The risk of over fi tting was noted previously [6,13].”
• Convolutional neural networks: an overview and application in radiology (2018, methodology): “For example, Setio et al. [39] used a multistream CNN to classify nodule candidates of chest CT images between nodules or non-nodules in the databases of the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) [40], ANODE09 [41], and the Danish Lung Cancer Screening…”
• Deep learning in medical imaging and radiation therapy. (2018, background): “166,168 More recently, 3D CNNs that use 3D convolution kernels are successfully replacing 2D CNNs for volumetric data.”
• Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning (2017, methodology): “My group used the same dataset for our convolutional network based nodule detection system [48], and we were curious to learn from experiences of other groups working with the same data.”
• A survey on deep learning in medical image analysis (2017, methodology): “Setio et al. (2016) used a multi-stream CNN to classify points of”