Armato III, S.G., McLennan, G., Bidaut, L., McNitt-Gray, M.F., Meyer, C.R., Reeves, A.P., Zhao, B., Aberle, D.R., Henschke, C.I., Hoffman, E.A., et al., 2011. The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical physics 38, 915–931.
Article PubMed PubMed Central Google Scholar
Baratella, E., Carbi, M., Minelli, P., Segalotti, A., Ruaro, B., Salton, F., Polverosi, R., Cova, M.A., 2025. Calcified lung nodules: A diagnostic challenge in clinical daily practice. Tomography 11, 28.
Article PubMed PubMed Central Google Scholar
Bbosa, R., Gui, H., Luo, F., Liu, F., Efio-Akolly, K., Chen, Y.P.P., 2024. Mrunet-3d: A multi-stride residual 3d unet for lung nodule segmentation. Methods 226, 89–101.
Article CAS PubMed Google Scholar
Ben-Assuli, O., Sagi, D., Leshno, M., Ironi, A., Ziv, A., 2015. Improving diagnostic accuracy using ehr in emergency departments: A simulation-based study. Journal of Biomedical Informatics 55, pages31–40. https://doi.org/10.1016/j.jbi.2015.03.004.
Bi, W.L., Hosny, A., Schabath, M.B., Giger, M.L., Birkbak, N.J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I.F., et al., 2019. Artificial intelligence in cancer imaging: clinical challenges and applications. CA: a cancer journal for clinicians 69, pages127–157.
Borg, M., Rasmussen, T.R., Hilberg, O., 2024. Introduction of the danish lung nodule registry: A part of the danish lung cancer registry. Cancer epidemiology 89, 102543.
Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R.L., Soerjomataram, I., Jemal, A., 2024. Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 74, 229–263.
Cai, Y., Liu, Z., Zhang, Y., Yang, Z., 2024. Mdfn: A multi-level dynamic fusion network with self-calibrated edge enhancement for lung nodule segmentation. Biomedical Signal Processing and Control 87, 105507.
Cheng, J., Ye, J., Deng, Z., Chen, J., Li, T., Wang, H., Su, Y., Huang, Z., Chen, J., Jiang, L., Sun, H., He, J., Zhang, S., Zhu, M., Qiao, Y., 2023. Sam-med2d. arxiv:2308.16184
Chiu, C.H., Yang, P.C., 2024. Challenges of lung cancer control in asia. EClinicalMedicine 74.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv:2010.11929.
Fan, R., Chen, J., Xu, S., Wu, W., Yi, J., Zhang, N., Chang, M., Dong, X., Chen, X., Zou, Y., Li, X., Wu, Y., 2024. T-stage diagnosis of lung cancer based on deep learning in ct images. Digital Medicine 10, e00017. https://doi.org/10.1097/DM-2024-00017.
Frangioni, J.V., 2008. New technologies for human cancer imaging. Journal of clinical oncology 26, 4012–4021.
Article PubMed PubMed Central Google Scholar
Gao, G., Lai, H., Jia, Z., (2024a). Munet++: Multilevel wavelet nested unet++ demoiréing residual network. Displays 83, 102741.
Gao, S., Xu, Z., Kang, W., Lv, X., Chu, N., Xu, S., Hou, D., 2024b. Artificial intelligence-driven computer aided diagnosis system provides similar diagnosis value compared with doctors’ evaluation in lung cancer screening. BMC Medical Imaging 24.
Gedam, A.N., Rumale, A.S., 2024. A hybrid optimization approach for pulmonary nodules segmentation and classification using deep cnn. EAI Endorsed Transactions on Pervasive Health and Technology 10.
He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.
Investigators, I.E.L.C.A.P., 2006. Survival of patients with stage i lung cancer detected on ct screening. New England Journal of Medicine 355, 1763–1771.
Jang, J., Kyung, D., Kim, S.H., Lee, H., Bae, K., Choi, E., 2024. Significantly improving zero-shot x-ray pathology classification via fine-tuning pre-trained image-text encoders. Scientific Reports 14, 23199.
Article CAS PubMed PubMed Central Google Scholar
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.Y., et al., 2023. Segment anything, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 4015–4026.
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., Van Der Laak, J.A., Van Ginneken, B., Sánchez, C.I., 2017. A survey on deep learning in medical image analysis. Medical image analysis 42, 60–88.
Ma, J., Kim, S., Li, F., Baharoon, M., Asakereh, R., Lyu, H., Wang, B., 2024. Segment anything in medical images and videos: Benchmark and deployment. arXiv preprint arXiv:2408.03322 .
de Margerie-Mellon, C., Chassagnon, G., 2023. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagnostic and Interventional Imaging 104, 11–17.
McWilliams, A., Tammemagi, M.C., Mayo, J.R., Roberts, H., Liu, G., Soghrati, K., Yasufuku, K., Martel, S., Laberge, F., Gingras, M., Atkar-Khattra, S., Berg, C.D., Evans, K., Finley, R., Yee, J., English, J., Nasute, P., Goffin, J., Puksa, S., Stewart, L., Tsai, S., Johnston, M.R., Manos, D., Nicholas, G., Goss, G.D., Seely, J.M., Amjadi, K., Tremblay, A., Burrowes, P., MacEachern, P., Bhatia, R., Tsao, M.S., Lam, S., 2013. Probability of cancer in pulmonary nodules detected on first screening ct. New England Journal of Medicine 369, 910–919. https://doi.org/10.1056/NEJMoa1214726.
Article CAS PubMed PubMed Central Google Scholar
Niu, C., Wang, G., 2022. Unsupervised contrastive learning based transformer for lung nodule detection. Physics in Medicine & Biology 67, 204001.
Pairon, J.C., Laurent, F., Rinaldo, M., Clin, B., Andujar, P., Ameille, J., Brochard, P., Chammings, S., Ferretti, G., Galateau-Sallé, F., Gislard, A., Letourneux, M., Luc, A., Schorlé, E., Paris, C., 2013. Pleural plaques and the risk of pleural mesothelioma. JNCI: Journal of the National Cancer Institute 105, 293–301. https://doi.org/10.1093/jnci/djs513.
Qin, Z., Yi, H., Lao, Q., Li, K., 2022. Medical image understanding with pretrained vision language models: A comprehensive study. arXiv preprint arXiv:2209.15517 .
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al., 2021. Learning transferable visual models from natural language supervision, in: International conference on machine learning, organizationPmLR. pp. 8748–8763.
Ravi, N., Gabeur, V., Hu, Y.T., Hu, R., Ryali, C., Ma, T., Khedr, H., Rädle, R., Rolland, C., Gustafson, L., et al., 2024. Sam 2: Segment anything in images and videos. arXiv preprint arXiv:2408.00714 .
Selvadass, S., Bruntha, P.M., Sagayam, K.M., Günerhan, H., 2024. Satunet: Series atrous convolution enhanced u-net for lung nodule segmentation. International Journal of Imaging Systems and Technology 34, e22964.
Setio, A.A.A., Traverso, A., De Bel, T., Berens, M.S., Van Den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M.E., Geurts, B., et al., 2017. Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Medical image analysis 42, 1–13.
Shaukat, F., Anwar, S.M., Parida, A., Lam, V.K., Linguraru, M.G., Shah, M., 2024. Lung-cadex: Fully automatic zero-shot detection and classification of lung nodules in thoracic ct images, in: International Workshop on Machine Learning in Medical Imaging, organizationSpringer. pp. 73–82.
Shaukat, F., Raja, G., Frangi, A.F., 2019. Computer-aided detection of lung nodules: a review. Journal of Medical Imaging 6, 020901–020901.
Shaukat, F., Raja, G., Gooya, A., Frangi, A.F., 2017. Fully automatic detection of lung nodules in ct images using a hybrid feature set. Medical physics 44, 3615–3629.
Siegel, R.L., Giaquinto, A.N., Jemal, A., 2024. Cancer statistics, 2024. CA: a cancer journal for clinicians 74, 12–49.
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Amin, M., Hou, L., Clark, K., Pfohl, S.R., Cole-Lewis, H., et al., 2025. Toward expert-level medical question answering with large language models. Nature Medicine 31, 943–950.
Article CAS PubMed PubMed Central Google Scholar
Sun, L., Zhang, M., Lu, Y., Zhu, W., Yi, Y., Yan, F., 2024. Nodule-clip: Lung nodule classification based on multi-modal contrastive learning. Computers in Biology and Medicine 175, 108505. https://doi.org/10.1016/j.compbiomed.2024.108505.
Sun, R., Pang, Y., Li, W., 2023. Efficient lung cancer image classification and segmentation algorithm based on an improved swin transformer. Electronics 12, 1024.
Thawkar, O., Shaker, A., Mullappilly, S.S., Cholakkal, H., Anwer, R.M., Khan, S., Laaksonen, J., Khan, F.S., 2023. Xraygpt: Chest radiographs summarization using medical vision-language models. arXiv preprint arXiv:2306.07971 .
UrRehman, Z., Qiang, Y., Wang, L., Shi, Y., Yang, Q., Khattak, S.U., Aftab, R., Zhao, J., 2024. Effective lung nodule detection using deep cnn with dual attention mechanisms. Scientific Reports 14, 3934.
Article CAS PubMed PubMed Central Google Scholar
Wang, H., Zhu, H., Ding, L., 2022. Accurate classification of lung nodules on ct images using the transunet. Frontiers in Public Health 10. https://doi.org/10.3389/fpubh.2022.1060798.
Wang, L., Zhang, C., Li, J., 2024. A hybrid cnn-transformer model for predicting n staging and survival in non-small cell lung cancer patients based on ct-scan. Tomography 10, 1676–1693. https://doi.org/10.3390/tomography10100123.
Article PubMed PubMed Central Google Scholar
Wu, C., Zhang, X., Zhang, Y., Hui, H., Wang, Y., Xie, W., 2025. Towards generalist foundation model for radiology by leveraging web-scale 2d&3d medical data. Nature Communications 16, 7866.
Article CAS PubMed PubMed Central Google Scholar
Xu, P., Zhu, X., Clifton, D.A., 2023. Multimodal learning with transformers: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 12113–12132.
Zhang, J., Huang, J., Jin, S., Lu, S., 2024. Vision-language models for vision tasks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence .
Comments (0)