Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.

By S. Armato, Feng Li, Maryellen Lissak Giger, H. MacMahon, S. Sone, Kunio Doi. Radiology, 2002

306 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/d4d8001eb1f15a44bdaed15f825c997054932538

How researchers see this paper

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

In Artificial Intelligence

1. Mathias Prokop
2. Hiroshi Fujita
3. Ingrid C. Sluimer
4. Meindert Niemeijer
5. Keelin Murphy
6. B. van Ginneken

In Pulmonary and Respiratory Medicine

1. B. van Ginneken
2. Hiroshi Fujita
3. Mathias Prokop
4. Ingrid C. Sluimer
5. Arnold M. R. Schilham
6. Meindert Niemeijer

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. Hiroshi Fujita
3. Meindert Niemeijer
4. Mathias Prokop
5. Arnold M. R. Schilham
6. Ingrid C. Sluimer
7. Keelin Murphy
8. Josien P. W. Pluim

Citations in the corpus

• Computer analysis of computed tomography scans of the lung: a survey (2006, background): “In later papers, Armato and co-workers concentrated on schemes to reduce the number of FPs: rule-based [135], [136], LDA [136], [137], and massive training ANN [136], [138].”
• Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study (2010, background): “A large number of systems for nodule detection have been proposed in the literature (Li et al., 2008; Arimura et al., 2004; Armato et al., 2001, 2002; Bae et al., 2005; Bellotti et al., 2007; Brown et al., 2003; Dehmeshki et al., 2007; Enquobahrie et al., 2007; Farag et al., 2004; Ge et al., 2005;…”