Sung, H. et al. World most cancers statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 international locations. CA: a most cancers journal for clinicians 71, 209–249 (2021).
Siegel, R. L., Giaquinto, A. N. & Jemal, A. Most cancers statistics 2024. CA: a most cancers journal for clinicians. 74(1), 12–49 (2024).
Rorke, L. B. Pathologic analysis because the gold customary. Most cancers 79, 665–667 (1997).
Wang, S., Ouyang, X., Liu, T., Wang, Q. & Shen, D. Comply with my eye: Utilizing gaze to oversee computer-aided analysis. IEEE Trans. Med. Imaging. 41, 1688–1698 (2022).
Reis, E. P. et al. BRAX, Brazilian labeled chest x-ray dataset. Sci. Knowledge. 9, 487 (2022).
Nationwide Lung Screening Trial Analysis Staff. Diminished lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medication 365, 395–409 (2011).
Singh, S. P. et al. Reader variability in figuring out pulmonary nodules on chest radiographs from the nationwide lung screening trial. Journal of thoracic imaging 27(4), 249–254 (2012).
Infante, M. et al. A randomized examine of lung most cancers screening with spiral computed tomography: three-year outcomes from the DANTE trial. American journal of respiratory and significant care medication 180(5), 445–453 (2009).
Mei, J., Cheng, M., Xu, G., Wan, L. & Zhang, H. SANet: A slice-aware community for pulmonary nodule detection. IEEE Trans. Sample Anal. Machine Intell. 44, 4374–4387, https://doi.org/10.1109/TPAMI.2021.3065086 (2021).
Liao, F., Liang, M., Li, Z., Hu, X. & Track, S. Consider the malignancy of pulmonary nodules utilizing the 3-D deep leaky noisy-or community. IEEE Trans. Neural Netw. Study. Syst. 30, 3484–3495, https://doi.org/10.1109/TNNLS.2019.2892409 (2019).
Shin, H. C., Orton, M. R., Collins, D. J., Doran, S. J. & Leach, M. O. Stacked autoencoders for unsupervised function studying and a number of organ detection in a pilot examine utilizing 4D affected person information. IEEE Trans. Sample Anal. Mach. Intell. 35, 1930–1943 (2012).
Seidlitz, S. et al. Strong deep learning-based semantic organ segmentation in hyperspectral photos. Medical Picture Evaluation 80, 102488 (2022).
Jacobsab, C. et al. Automated detection of subsolid pulmonary nodules in thoracic computed tomography photos. Medical Picture Evaluation 18, 374–384 (2014).
Duggan, N. et al. A way for lung nodule candidate detection in CT utilizing international minimization strategies. Worldwide workshop on power minimization strategies in pc imaginative and prescient and sample recognition. 478-491 (2015).
Messay, T., Hardie, R. C. & Rogers, S. Okay. A brand new computationally environment friendly CAD system for pulmonary nodule detection in CT imagery. Medical Picture Evaluation 14, 390–406 (2010).
Jacobs, C. et al. Automated detection of subsolid pulmonary nodules in thoracic computed tomography photos. Medical Picture Evaluation 18, 374–384 (2014).
Girshick, R et al. Wealthy function hierarchies for correct object detection and semantic segmentation. Proceedings of the IEEE convention on pc imaginative and prescient and sample recognition. 580-587 (2014).
Luo, X. et al. SCPM-Internet: An anchor-free 3D lung nodule detection community utilizing sphere illustration and middle factors matching. Medical Picture Evaluation 75, 102287 (2022).
Ali, Z., Irtaza, A. & Maqsood, M. An environment friendly U-Internet framework for lung nodule detection utilizing densely related dilated convolutions. The Journal of Supercomputing 78, 1602–1623 (2022).
Sahu, S., Londhe, N. & Verma, S. Pulmonary nodule detection in CT photos utilizing optimum multilevel thresholds and rule-based filtering. IETE Journal of Analysis 68, 265–282 (2022).
Setio, A. et al. Pulmonary nodule detection in CT photos: false optimistic discount utilizing multi-view convolutional networks. IEEE Trans. Med. Imaging. 35, 1160–1169 (2016).
Ding, J., Li, A., Hu, Z. & Wang, L. Correct pulmonary nodule detection in computed tomography photos utilizing deep convolutional neural networks. Worldwide Convention on Medical Picture Computing and Laptop-Assisted Intervention. 559-567 (2017).
Li, Y., Fan, Y. DeepSEED: 3D squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection. 2020 IEEE seventeenth Worldwide Symposium on Biomedical Imaging (ISBI). 1866-1869 (2020).
Kim, B., Yoon, J., Choi, J. & Suk, H. Multi-scale gradual integration CNN for false optimistic discount in pulmonary nodule detection. Neural Networks 115, 1–10 (2019).
Shen, W. et al. Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Sample Recognition 61, 663–673 (2017).
Xie, Y. et al. Data-based collaborative deep studying for benign-malignant lung nodule classification on chest CT. IEEE Trans. Med. Imaging. 38, 991–1004 (2018).
Xie, Y., Zhang, J., Xia, Y., Fulham, M. & Zhang, Y. Fusing texture, form and deep model-learned info at determination stage for automated classification of lung nodules on chest CT. Data Fusion 42, 102–110 (2018).
Xie, Y., Zhang, J. & Xia, Y. Semi-supervised adversarial mannequin for benign-malignant lung nodule classification on chest CT. Medical Picture Evaluation 57, 237–248 (2019).
Li, R., Xiao, C., Huang, Y., Hassan, H. & Huang, B. Deep studying functions in computed tomography photos for pulmonary nodule detection and analysis: A overview. Diagnostics 12, 298, https://doi.org/10.3390/diagnostics12020298 (2022).
Armato, S. III et al. The lung picture database consortium (LIDC) and picture database useful resource initiative (IDRI): a accomplished reference database of lung nodules on CT scans. Medical physics 38, 915–931, https://doi.org/10.1118/1.3528204 (2011).
Shao, Y. et al. LIDP: A Lung Picture Dataset with Pathological Data for Lung Most cancers Screening. Worldwide Convention on Medical Picture Computing and Laptop-Assisted Intervention. 770–779, https://doi.org/10.1007/978-3-031-16437-8_74 (2022).
Setio, A. et al. Validation, comparability, and mixture of algorithms for automated detection of pulmonary nodules in computed tomography photos: the LUNA16 problem. Medical Picture Evaluation 42, 1–13, https://doi.org/10.1016/j.media.2017.06.015 (2017).
Sousa, J. et al. Lung Segmentation in CT Photos: A Residual U-Internet Method on a Cross-Cohort Dataset. Utilized Sciences 12, 1959, https://doi.org/10.3390/app12041959 (2022).
Cengil, E. & Cinar, A. A deep studying primarily based strategy to lung most cancers identification. 2018 Worldwide Convention on Synthetic Intelligence and Knowledge Processing (IDAP). 1–5, https://doi.org/10.1109/IDAP.2018.862072 (2018).
Jian, M. et al. A Cross Spatio-Temporal Pathology-based Lung Nodule Dataset. Preprint at https://arxiv.org/abs/2406.18018 (2024).
Jian, M. et al. A Lung Nodule Dataset with Histopathology-based Most cancers Kind Annotation. Zenodo https://doi.org/10.5281/zenodo.8422229 (2024).
Jian, M. et al. A Lung Nodule Dataset with Histopathology-based Most cancers Kind Annotation. Zenodo https://doi.org/10.5281/zenodo.11024613 (2024).
He, Okay., Zhang, X., Ren, S. & Solar, J. Deep residual studying for picture recognition. Proceedings of the IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition. 770-778 (2016).
Tan, M. & Le, Q. Efficientnet: Rethinking mannequin scaling for convolutional neural networks. Int. Conf. Mach. Study. 6105–6114 (2019).
He, A., Li, T., Li, N., Wang, Okay. & Fu, H. CABNet: class consideration block for imbalanced Diabetic Retinopathy grading. IEEE Trans. Med. Imaging. 40, 143–153 (2020).
Xie, S., Girshick, R., Dollár, P., Tu, Z. & He, Okay. Aggregated residual transformations for deep neural networks. Proceedings of the IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition. 1492-1500 (2017).
Gao, S. H. et al. Res2net: A brand new multi-scale spine structure. IEEE Trans. Sample Anal. Mach. Intell. 43, 652–662 (2019).
Hu, J., Shen, L. & Solar, G. Squeeze-and-excitation networks. Proceedings of the IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition. 7132–7141 (2018).
Dosovitskiy, A. et al. A picture is price 16×16 phrases: Transformers for picture recognition at scale. Worldwide Convention on Studying Representations. (2021).
Szegedy, C., Ioffe, S., Vanhoucke, V. & Alemi, A. Inception-v4, inception-resnet and the influence of residual connections on studying. Proceedings of the AAAI Convention on Synthetic Intelligence. 31 (2017).
Liu, Z. et al. A convnet for the 2020s. Proceedings of the IEEE/CVF Convention on Laptop Imaginative and prescient and Sample Recognition. 11976–11986 (2022).
Liu, Z. et al. Swin transformer: Hierarchical imaginative and prescient transformer utilizing shifted home windows. Proceedings of the IEEE/CVF Worldwide Convention on Laptop Imaginative and prescient. 10012–10022 (2021).
Ren, S., He, Okay., Girshick, R. & Solar, J. Sooner r-cnn: In direction of real-time object detection with area proposal networks. Adv. Neural Inf. Course of. Syst. 28, (2015).
Redmon, J. & Farhadi, A. Yolov3: An incremental enchancment. Preprint at https://arxiv.org/abs/1804.02767 (2018).
Howard, A. G. et al. Mobilenets: Environment friendly convolutional neural networks for cellular imaginative and prescient functions. Preprint at https://arxiv.org/abs/1704.04861 (2017).
Liu, W. et al. Ssd: Single shot multibox detector. Laptop Imaginative and prescient–ECCV 2016: 14th European Convention. 21–37 (2016).
Lin, T. Y., Goyal, P., Girshick, R., He, Okay. & Dollár, P. Focal loss for dense object detection. Proceedings of the IEEE Worldwide Convention on Laptop Imaginative and prescient. 2980-2988 (2017).

