A new computationally efficient CAD system for pulmonary nodule detection in CT imagery

By Temesguen Messay, Russell C. Hardie, Steven K. Rogers. Medical Image Anal., 2010

474 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0739ab265165063f6eeff73084ea6b07ff200f35

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. Qi-Yan Dou
3. Weidong (Tom) Cai
4. Mathias Prokop
5. D. Feng
6. P. Heng
7. Michael J. Fulham
8. Lequan Yu

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. P. Heng
3. Hao Chen
4. Jing Qin
5. Qi-Yan Dou
6. Lequan Yu
7. Francesco Ciompi
8. Geert J. S. Litjens

Citations in the corpus

• Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge (2016, methodology): “…(2013); Teramoto and Fujita (2013); Cascio et al. (2012); Guo and Li (2012); Camarlinghi et al. (2011); Tan et al. (2011); Riccardi et al. (2011); Messay et al. (2010); Golosio et al. (2009); Murphy et al. (2009)), there have only been few studies providing an objective comparative evaluation…”
• Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT (2019, methodology): “The translation step was selected from [0, 6] voxels, and the rotation angle was randomly selected from {90°, 180°, 270°}.”
• Towards automatic pulmonary nodule management in lung cancer screening with deep learning (2016, methodology): “34 and linear SVM classifier, (3) the proposed deep learning approach using ConvNets working at 1, 2 and 3 scales.”
• Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection (2017, background): “Based on reliable detection of lung nodules, radiologists and surgeons can perform size measurements and appearance characterizations for cancer malignancy diagnosis [6] and, if necessary, timely surgical intervention in order to increase the survival chances of patients [7], [8].”
• Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks (2016, methodology): “Simple techniques such as double thresholding and morphological operations are often used to detect a large set of candidates [4], [5].”