Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: The ANODE09 study

By B. van Ginneken, S. Armato, B. Hoop, S. V. D. Vorst, T. Duindam, Meindert Niemeijer, Keelin Murphy, Arnold M. R. Schilham, A. Retico, M. Fantacci and 14 more. Medical Image Anal., 2010

316 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/39ae90f765547171cc6163ba3ab2e977c86c03d3

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

3D G-CNNs for Pulmonary Nodule Detection (2018, methodology): “5mm, allowed us to use the reference standard for nodule detection as used by the LUNA16 grand challenge (26) and performance metric as specified by the ANODE09 study (27).”
Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: The LUNA16 challenge (2016, methodology): “…19,004 “non-nodule” annotations) were considered “ir-relevant ﬁndings” and marks on these locations were 3 https://luna16.grand-challenge.org/ not counted as false positives nor as true positives in the ﬁnal analysis; the same approach was used by (van Ginneken et al. (2010); Jacobs et al. (2016)).”
Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network (2017, methodology): “[17] gave a comprehensive review of six conventional algorithms and evaluated them on the ANODE09 data set, which contains 55 scans.”
Pulmonary Nodule Detection in CT Images: False Positive Reduction Using Multi-View Convolutional Networks (2016, methodology): “Although it has been shown that CAD systems improve the reading efficiency of radiologists, a considerable number of nodules remains undetected at low false positive rates, prohibiting the use of CAD in clinical practice [6], [7].”
Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning (2017, methodology): “In 2009, I organized a comparative study called Automated NOdule Detection (ANODE09 3 ) [46].”
Metrics Reloaded: Recommendations for image analysis validation (2022, background): “Average Precision AP [57] [0, 1] ↑ x x x Free-Response Receiver Operating Characteristic Score FROC Score [8, 89] [0, 1] ↑ x x”