Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin D, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–86.

Article  Google Scholar 

Pehrson L, Nielsen M, Lauridsen C. Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: A systematic review. Diagnostics. 2019;9:29.

Article  Google Scholar 

Gu Y, Chi J, Liu J, Yang L, Zhang B, Yu D, Zhao Y, Lu X. A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning. Comput Biol Med. 2021;137:104806.

Article  Google Scholar 

Khosravan N, Bagci U. S4ND: single-shot single-scale lung nodule detection. In: International conference on medical image computing and computer-assisted intervention. Springer; 2018. pp. 794–802.

Pezeshk A, Hamidian S, PetrickN., and, Sahiner B. 3-D convolutional neural networks for automatic detection of pulmonary nodules in chest CT. IEEE J Biomedical Health Inf. 2019;23(5):2080–90.

Article  Google Scholar 

Kim B, Yoon Y, Choi J, Heung-II S. Multi-scale gradual integration CNN for false positive reduction in pulmonary nodule detection. Neural Netw. 2019;115:1–10.

Article  Google Scholar 

Zheng S, Guo J, Cui X, Veldhuis R, Oudkerk M, van Ooijen P. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection. IEEE Trans Med Imaging. 2020;39(3):797–805.

Article  Google Scholar 

Luo X, Song T, Wang G, Chen J, Chen Y, Li K, Metaxas D, Zhang S. SCPM-Net: an anchor-free 3D lung nodule detection network using sphere representation and center points matching. Med Image Anal. 2022;75:102287.

Article  Google Scholar 

Qin Y, Zheng H, Huang X, Yang J, Zhu Y. Pulmonary nodule segmentation with CT sample synthesis using adversarial networks. Med Phys. 2019;46(3):1218–29.

Article  Google Scholar 

Chen Q, Xie W, Zhou P, Zheng C, Wu D. Multi-crop convolutional neural networks for fast lung nodule segmentation. IEEE Trans Emerg Top Comput Intell. 2022;6(5):1190–200.

Article  Google Scholar 

Budd S, Robinson E, Kainz B. A survey on active learning and human-in-the-loop deep learning for medical image analysis. Med Image Anal. 2021;71:102062.

Article  Google Scholar 

Gaillochet M, Desrosiers C, Lombaert H. Active learning for medical image segmentation with stochastic batches. Med Image Anal. 2023;90:102958.

Article  Google Scholar 

Setio A, Traverso A, De Bel T, et al. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the Luna16 challenge. Med Image Anal. 2017;42:1–13.

Article  Google Scholar 

Milletari F, Navab N, Ahmadi S. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: The 2016 fourth international conference on 3D vision (3DV); 2016. pp. 565–571.

Ronneberger O, Fischer P, Brox B. ‘U-Net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing & computer-assisted intervention. Cham: Springer; 2015. pp. 234–241.

Zheng H, Qin Y, Gu Y et al. Springer, Strasbourg, France,. Refined local imbalance- based weight for airway segmentation in CT. In: Medical image computing and computer assisted intervention-MICCAI 2021: 24th international conference; 2021. pp. 410–419.

Zhou Z, Siddiquee M, Tajbakhsh N, et al. Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging. 2019;39(6):1856–67.

Article  Google Scholar 

Zhi L, Jiang W, Zhang S, et al. Deep neural network pulmonary nodule segmentation methods for CT images: literature review and experimental comparisons. Comput Biol Med. 2023;164:107321.

Article  Google Scholar