@@ -70,6 +70,18 @@ Luna results are listed under Benchmarks
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#### References
- S. G. Armato III, G. McLennan, L. Bidaut, M. F. McNitt-Gray, C. R. Meyer, A. P.Reeves, B. Zhao, D. R. Aberle, C. I. Henschke, E. A. Hoffman, et al. The lungimage database consortium (lidc) and image database resource initiative (idri):a completed reference database of lung nodules on ct scans.Medical physics,38(2):915–931, 2011
- L. Jin, J. Yang, K. Kuang, B. Ni, Y. Gao, Y. Sun, P. Gao, W. Ma, M. Tan, H. Kang,J. Chen, and M. Li. Deep-learning-assisted detection and segmentation of ribfractures from CT scans: Development and validation of FracNet. 62. Publisher:Elsevier
- C. Tabea Kossen, L. Kaufhold, M. H ̈ullebrand, J.-M. Kuhnigk, J. Br ̈uhning,J. Schaller, B. Pfahringer, A. Spuler, L. Goubergrits, and A. Hennemuth. Cerebralaneurysm detection and analysis, Mar. 2020
- K. Timmins, E. Bennink, I. van der Schaaf, B. Velthuis, Y. Ruigrok, and H. Kuijf.Intracranial Aneurysm Detection and Segmentation Challenge, Mar. 2020.
- N. Heller, N. Sathianathen, A. Kalapara, E. Walczak, K. Moore, H. Kaluzniak,J. Rosenberg, P. Blake, Z. Rengel, M. Oestreich, et al. The kits19 challenge data:300 kidney tumor cases with clinical context, ct semantic segmentations, and sur-gical outcomes.arXiv preprint arXiv:1904.00445, 2019
- G. Litjens, O. Debats, J. Barentsz, N. Karssemeijer, and H. Huisman. Computer-aided detection of prostate cancer in mri.IEEE TMI, 33(5):1083–1092, 2014
- R. Cuocolo, A. Comelli, A. Stefano, V. Benfante, N. Dahiya, A. Stanzione,A. Castaldo, D. R. D. Lucia, A. Yezzi, and M. Imbriaco. Deep learning whole-gland and zonal prostate segmentation on a public mri dataset.Journal of Mag-netic Resonance Imaging, 2021.
- A. L. Simpson, M. Antonelli, S. Bakas, M. Bilello, K. Farahani, B. Van Ginneken,A. Kopp-Schneider, B. A. Landman, G. Litjens, B. Menze, et al. A large anno-tated medical image dataset for the development and evaluation of segmentationalgorithms.arXiv preprint arXiv:1902.09063, 2019.
- H. R. Roth, L. Lu, A. Seff, K. M. Cherry, J. Hoffman, S. Wang, J. Liu, E. Turkbey,and R. M. Summers. A new 2.5 d representation for lymph node detection usingrandom sets of deep convolutional neural network observations. InMICCAI, pages520–527. Springer, 2014
- A. Seff, L. Lu, A. Barbu, H. Roth, H.-C. Shin, and R. M. Summers. Leveraging mid-level semantic boundary cues for automated lymph node detection. InMICCAI,pages 53–61. Springer, 2015
<sup>*</sup> Some of the other methods also use FPR stages but the methods listed below report results w. and wo. FPR.
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#### References (no particular oder)
- A. A. A. Setio, A. Traverso, T. de Bel, M. S. Berens, C. van den Bogaard, P. Cerello,H. Chen, Q. Dou, M. E. Fantacci, B. Geurts, R. van der Gugten, P. A. Heng,B. Jansen, M. M. de Kaste, V. Kotov, J. Y.-H. Lin, J. T. Manders, A. S ́o ̃nora-Mengana, J. C. Garc ́ıa-Naranjo, E. Papavasileiou, M. Prokop, M. Saletta, C. M.Schaefer-Prokop, E. T. Scholten, L. Scholten, M. M. Snoeren, E. L. Torres, J. Van-demeulebroucke, N. Walasek, G. C. Zuidhof, B. van Ginneken, and C. Jacobs.Validation, comparison, and combination of algorithms for automatic detection ofpulmonary nodules in computed tomography images: The luna16 challenge.Me-dIA, 42:1–13, 2017.
- Z. Gong, D. Li, J. Lin, Y. Zhang and K. -M. Lam, "Towards Accurate Pulmonary Nodule Detection by Representing Nodules as Points With High-Resolution Network," in IEEE Access, vol. 8, pp. 157391-157402, 2020, doi: 10.1109/ACCESS.2020.3019104
- Q. Dou, H. Chen, L. Yu, J. Qin and P. Heng, "Multilevel Contextual 3-D CNNs for False Positive Reduction in Pulmonary Nodule Detection," in IEEE Transactions on Biomedical Engineering, vol. 64, no. 7, pp. 1558-1567, July 2017, doi: 10.1109/TBME.2016.2613502.
- Gupta, A., Saar, T., Martens, O. and Moullec, Y.L. (2018), Automatic detection of multisize pulmonary nodules in CT images: Large-scale validation of the false-positive reduction step. Med. Phys., 45: 1135-1149. https://doi.org/10.1002/mp.12746
- J. Ding, A. Li, Z. Hu, and L. Wang. Accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks. In MICCAI, pages 559–567. Springer, 2017
- Q. Dou, H. Chen, Y. Jin, H. Lin, J. Qin, and P.-A. Heng. Automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid-loss residual learning. In MICCAI, pages 630–638. Springer, 2017
- N. Khosravan and U. Bagci. S4nd: Single-shot single-scale lung nodule detection. In MICCAI, pages 794–802. Springer, 2018.
