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ECR 2019 / C-2065
Towards radiologist-level malignancy detection on chest CT scans: a comparative study of the performance of convolutional neural networks and four thoracic radiologists
Congress: ECR 2019
Poster No.: C-2065
Type: Scientific Exhibit
Keywords: Artificial Intelligence, Lung, CT, Computer Applications-Detection, diagnosis, Cancer
Authors: V. Venugopal1, A. VAIDYA2, A. AHUJA2, Y. Singh2, K. Vaidhya3, A. Raj3, V. Mahajan2, S. Vaidya4, A. Rangasai Devalla3; 1Aligarh/IN, 2New Delhi/IN, 3Bangalore/IN, 4Mumbai/IN


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  2.  Koning, Harry & Aalst, Carlijn & Haaf, Kevin & Oudkerk, M. (2018). PL02.05 Effects of Volume CT Lung Cancer Screening: Mortality Results of the NELSON Randomised-Controlled Population Based Trial. Journal of Thoracic Oncology. 13. S185. 10.1016/j.jtho.2018.08.012.
  3. Armato III, Samuel G., McLennan, Geoffrey, Bidaut, Luc, McNitt-Gray, Michael F., Meyer, Charles R., Reeves, Anthony P., … Clarke, Laurence P. (2015). Data from LIDC-IDRI. The Cancer Imaging Archive
  4. B. van Ginneken, S.G. Armato, B. de Hoop, S. van de Vorst, T. Duindam, M. Niemeijer, K. Murphy, A.M.R. Schilham, A. Retico, M.E. Fantacci, N. Camarlinghi, F. Bagagli, I. Gori, T. Hara, H. Fujita, G. Gargano, R. Belloti, F.D. Carlo, R. Megna, S. Tangaro, L. Bolanos, P. Cerello, S.C. Cheran, E.L. Torres and M. Prokop. "Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study", Medical Image Analysis 2010;14:707-722.
  5. Arnaud Arindra Adiyoso Setio et al (2016). Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the LUNA16 challenge. CoRR, abs/1612.08012
  6. Tsung-Yi Lin and (2016). Feature Pyramid Networks for Object Detection. CoRR, abs/1612.03144
  7. Fangzhou Liao and (2017). Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network. CoRR, abs/1711.08324



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