{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T02:16:04Z","timestamp":1768788964454,"version":"3.49.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 52377224"],"award-info":[{"award-number":["No. 52377224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 52377224"],"award-info":[{"award-number":["No. 52377224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 52377224"],"award-info":[{"award-number":["No. 52377224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 52377224"],"award-info":[{"award-number":["No. 52377224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"crossref","award":["No. 52377224"],"award-info":[{"award-number":["No. 52377224"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Central Guidance for Local Scientific and Technological Development Foundation","award":["No. 236Z7711G"],"award-info":[{"award-number":["No. 236Z7711G"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Foundation","award":["No. 236Z7711G"],"award-info":[{"award-number":["No. 236Z7711G"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Foundation","award":["No. 236Z7711G"],"award-info":[{"award-number":["No. 236Z7711G"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Foundation","award":["No. 236Z7711G"],"award-info":[{"award-number":["No. 236Z7711G"]}]},{"name":"Central Guidance for Local Scientific and Technological Development Foundation","award":["No. 236Z7711G"],"award-info":[{"award-number":["No. 236Z7711G"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2024,12]]},"DOI":"10.1007\/s00530-024-01532-4","type":"journal-article","created":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:42:30Z","timestamp":1730302950000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Expressive feature representation pyramid network for pulmonary nodule detection"],"prefix":"10.1007","volume":"30","author":[{"given":"Haochen","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lipeng","family":"Xing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingzhao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiyang","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"issue":"1","key":"1532_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21654","volume":"71","author":"RL Siegel","year":"2021","unstructured":"Siegel, R.L., Miller, K.D., Fuchs, H.E., Jemal, A., et al.: Cancer statistics, 2021. CA Cancer J. Clin. 71(1), 7\u201333 (2021)","journal-title":"CA Cancer J. Clin."},{"issue":"6","key":"1532_CR2","doi-asserted-by":"publisher","first-page":"345","DOI":"10.3322\/caac.20088","volume":"60","author":"T Gansler","year":"2010","unstructured":"Gansler, T., Ganz, P.A., Grant, M., Greene, F.L., Johnstone, P., Mahoney, M., Newman, L.A., Oh, W.K., Thomas, C.R., Jr., Thun, M.J., et al.: Sixty years of ca: a cancer journal for clinicians. CA Cancer J. Clin. 60(6), 345\u2013350 (2010)","journal-title":"CA Cancer J. Clin."},{"issue":"2","key":"1532_CR3","doi-asserted-by":"publisher","first-page":"230787","DOI":"10.1001\/jamanetworkopen.2023.0787","volume":"6","author":"RU Osarogiagbon","year":"2023","unstructured":"Osarogiagbon, R.U., Liao, W., Faris, N.R., Fehnel, C., Goss, J., Shepherd, C.J., Qureshi, T., Matthews, A.T., Smeltzer, M.P., Pinsky, P.F.: Evaluation of lung cancer risk among persons undergoing screening or guideline-concordant monitoring of lung nodules in the mississippi delta. JAMA Netw. Open 6(2), 230787\u2013230787 (2023)","journal-title":"JAMA Netw. Open"},{"issue":"5","key":"1532_CR4","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222976","volume":"307","author":"JH Lee","year":"2023","unstructured":"Lee, J.H., Hong, H., Nam, G., Hwang, E.J., Park, C.M.: Effect of human-ai interaction on detection of malignant lung nodules on chest radiographs. Radiology 307(5), 222976 (2023)","journal-title":"Radiology"},{"issue":"6","key":"1532_CR5","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/JBHI.2021.3135647","volume":"26","author":"J Song","year":"2021","unstructured":"Song, J., Huang, S.-C., Kelly, B., Liao, G., Shi, J., Wu, N., Li, W., Liu, Z., Cui, L., Lungre, M.P., et al.: Automatic lung nodule segmentation and intra-nodular heterogeneity image generation. IEEE J. Biomed. Health Inform. 26(6), 2570\u20132581 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"16","key":"1532_CR6","doi-asserted-by":"publisher","first-page":"19724","DOI":"10.1007\/s10489-023-04552-1","volume":"53","author":"MH Alshayeji","year":"2023","unstructured":"Alshayeji, M.H., Abed, S.: Lung cancer classification and identification framework with automatic nodule segmentation screening using machine learning. Appl Intell. 53(16), 19724\u201319741 (2023)","journal-title":"Appl Intell."},{"key":"1532_CR7","doi-asserted-by":"crossref","unstructured":"Ahmed, I., Chehri, A., Jeon, G., Piccialli, F.: Automated pulmonary nodule classification and detection using deep learning architectures. IEEE\/ACM Transactions on Computational Biology and Bioinformatics (2022)","DOI":"10.1109\/TCBB.2022.3192139"},{"key":"1532_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104806","volume":"137","author":"Y Gu","year":"2021","unstructured":"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. 137, 104806 (2021)","journal-title":"Comput. Biol. Med."},{"key":"1532_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105055","volume":"85","author":"RV Kaulgud","year":"2023","unstructured":"Kaulgud, R.V., Patil, A.: Analysis based on machine and deep learning techniques for the accurate detection of lung nodules from ct images. Biomed. Signal Process. Control 85, 105055 (2023)","journal-title":"Biomed. Signal Process. Control"},{"issue":"2","key":"1532_CR10","doi-asserted-by":"publisher","first-page":"298","DOI":"10.3390\/diagnostics12020298","volume":"12","author":"R Li","year":"2022","unstructured":"Li, R., Xiao, C., Huang, Y., Hassan, H., Huang, B.: Deep learning applications in computed tomography images for pulmonary nodule detection and diagnosis: a review. Diagnostics 12(2), 298 (2022)","journal-title":"Diagnostics"},{"issue":"1","key":"1532_CR11","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1007\/s11517-021-02462-3","volume":"60","author":"R Manickavasagam","year":"2022","unstructured":"Manickavasagam, R., Selvan, S., Selvan, M.: Cad system for lung nodule detection using deep learning with CNN. Med Biol Eng Comput 60(1), 221\u2013228 (2022)","journal-title":"Med Biol Eng Comput"},{"issue":"14","key":"1532_CR12","doi-asserted-by":"publisher","first-page":"4840","DOI":"10.3390\/jcm12144840","volume":"12","author":"Y Misumi","year":"2023","unstructured":"Misumi, Y., Nonaka, K., Takeuchi, M., Kamitani, Y., Uechi, Y., Watanabe, M., Kishino, M., Omori, T., Yonezawa, M., Isomoto, H., et al.: Comparison of the ability of artificial-intelligence-based computer-aided detection (cad) systems and endoscopists to detect colorectal neoplastic lesions on endoscopy video. J. Clin. Med. 12(14), 4840 (2023)","journal-title":"J. Clin. Med."},{"key":"1532_CR13","unstructured":"Shuvo, S.B.: An automated end-to-end deep learning-based framework for lung cancer diagnosis by detecting and classifying the lung nodules. arXiv preprint arXiv:2305.00046 (2023). Accessed 1 Dec 2023"},{"key":"1532_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104866","volume":"85","author":"H Mkindu","year":"2023","unstructured":"Mkindu, H., Wu, L., Zhao, Y.: Lung nodule detection in chest ct images based on vision transformer network with bayesian optimization. Biomed. Signal Process. Control 85, 104866 (2023)","journal-title":"Biomed. Signal Process. Control"},{"issue":"8","key":"1532_CR15","first-page":"4374","volume":"44","author":"J Mei","year":"2021","unstructured":"Mei, J., Cheng, M.-M., Xu, G., Wan, L.-R., Zhang, H.: Sanet: a slice-aware network for pulmonary nodule detection. IEEE Trans. Pattern Anal. Mach. Intell. 44(8), 4374\u20134387 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"1532_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106470","volume":"153","author":"J Xu","year":"2023","unstructured":"Xu, J., Ren, H., Cai, S., Zhang, X.: An improved faster R-CNN algorithm for assisted detection of lung nodules. Comput Biol Med 153, 106470 (2023)","journal-title":"Comput Biol Med"},{"key":"1532_CR17","doi-asserted-by":"crossref","unstructured":"Ji, Z., Wu, Y., Zeng, X., An, Y., Zhao, L., Wang, Z., Ganchev, I.: Lung nodule detection in medical images based on improved yolov5s. IEEE Access (2023)","DOI":"10.1109\/ACCESS.2023.3296530"},{"key":"1532_CR18","doi-asserted-by":"crossref","unstructured":"Lu, X., Zeng, N., Wang, X., Huang, J., Hu, Y., Fang, J., Liu, J.: Ffnet: an end-to-end framework based on feature pyramid network and filter network for pulmonary nodule detection. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp. 1\u20136 (2023). IEEE","DOI":"10.1109\/ISBI53787.2023.10230631"},{"key":"1532_CR19","doi-asserted-by":"crossref","unstructured":"Chi, J., Zhao, J., Wang, S., Yu, X., Wu, C.: Lgdnet: local feature coupling global representations network for pulmonary nodules detection. Med Biol Eng Comput. pp 1\u201314 (2024)","DOI":"10.1007\/s11517-024-03043-w"},{"key":"1532_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102495","volume":"66","author":"A Shakarami","year":"2021","unstructured":"Shakarami, A., Menhaj, M.B., Mahdavi-Hormat, A., Tarrah, H.: A fast and yet efficient yolov3 for blood cell detection. Biomed. Signal Process. Control 66, 102495 (2021)","journal-title":"Biomed. Signal Process. Control"},{"key":"1532_CR21","doi-asserted-by":"crossref","unstructured":"Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263\u20137271 (2017)","DOI":"10.1109\/CVPR.2017.690"},{"key":"1532_CR22","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337 (2016). Springer, New York","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"1532_CR23","doi-asserted-by":"crossref","unstructured":"Zlocha, M., Dou, Q., Glocker, B.: Improving retinanet for ct lesion detection with dense masks from weak recist labels. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part VI 22, pp. 402\u2013410 (2019). Springer, New York.","DOI":"10.1007\/978-3-030-32226-7_45"},{"issue":"3","key":"1532_CR24","first-page":"567","volume":"34","author":"IW Harsono","year":"2022","unstructured":"Harsono, I.W., Liawatimena, S., Cenggoro, T.W.: Lung nodule detection and classification from thorax CT-scan using retinanet with transfer learning. J King Saud Univ Comput Inf Sci 34(3), 567\u2013577 (2022)","journal-title":"J King Saud Univ Comput Inf Sci"},{"key":"1532_CR25","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"key":"1532_CR26","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9759\u20139768 (2020)","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"1532_CR27","doi-asserted-by":"crossref","unstructured":"Wang, N., Gao, Y., Chen, H., Wang, P., Tian, Z., Shen, C., Zhang, Y.: Nas-fcos: Fast neural architecture search for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11943\u201311951 (2020)","DOI":"10.1109\/CVPR42600.2020.01196"},{"key":"1532_CR28","doi-asserted-by":"crossref","unstructured":"Girshick, R.: Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440\u20131448 (2015)","DOI":"10.1109\/ICCV.2015.169"},{"key":"1532_CR29","doi-asserted-by":"crossref","unstructured":"Pang, J., Chen, K., Shi, J., Feng, H., Ouyang, W., Lin, D.: Libra r-cnn: Towards balanced learning for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 821\u2013830 (2019)","DOI":"10.1109\/CVPR.2019.00091"},{"key":"1532_CR30","doi-asserted-by":"crossref","unstructured":"Lu, X., Li, B., Yue, Y., Li, Q., Yan, J.: Grid r-cnn. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7363\u20137372 (2019)","DOI":"10.1109\/CVPR.2019.00754"},{"key":"1532_CR31","doi-asserted-by":"crossref","unstructured":"Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6154\u20136162 (2018)","DOI":"10.1109\/CVPR.2018.00644"},{"key":"1532_CR32","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., Dai, J.: Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159 (2020). Accessed 15 Oct 2023"},{"key":"1532_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.107149","volume":"163","author":"Z Xu","year":"2023","unstructured":"Xu, Z., Zhang, X., Zhang, H., Liu, Y., Zhan, Y., Lukasiewicz, T.: Efpn: effective medical image detection using feature pyramid fusion enhancement. Comput Biol Med. 163, 107149 (2023)","journal-title":"Comput Biol Med."},{"key":"1532_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106786","volume":"220","author":"Y-S Huang","year":"2022","unstructured":"Huang, Y.-S., Chou, P.-R., Chen, H.-M., Chang, Y.-C., Chang, R.-F.: One-stage pulmonary nodule detection using 3-d DCNN with feature fusion and attention mechanism in CT image. Comput. Methods Programs Biomed. 220, 106786 (2022)","journal-title":"Comput. Methods Programs Biomed."},{"key":"1532_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106726","volume":"157","author":"H Jiang","year":"2023","unstructured":"Jiang, H., Diao, Z., Shi, T., Zhou, Y., Wang, F., Hu, W., Zhu, X., Luo, S., Tong, G., Yao, Y.-D.: A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Comput Biol Med. 157, 106726 (2023)","journal-title":"Comput Biol Med."},{"key":"1532_CR36","doi-asserted-by":"crossref","unstructured":"Zhang, H., Xu, Z., Yao, D., Zhang, S., Chen, J., Lukasiewicz, T.: Multi-head feature pyramid networks for breast mass detection. In: ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1\u20135 (2023). IEEE","DOI":"10.1109\/ICASSP49357.2023.10095967"},{"issue":"3","key":"1532_CR37","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1007\/s00521-022-07953-4","volume":"35","author":"P Celard","year":"2023","unstructured":"Celard, P., Iglesias, E., Sorribes-Fdez, J., Romero, R., Vieira, A.S., Borrajo, L.: A survey on deep learning applied to medical images: from simple artificial neural networks to generative models. Neural Comput. Appl. 35(3), 2291\u20132323 (2023)","journal-title":"Neural Comput. Appl."},{"key":"1532_CR38","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"1532_CR39","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759\u20138768 (2018)","DOI":"10.1109\/CVPR.2018.00913"},{"key":"1532_CR40","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10781\u201310790 (2020)","DOI":"10.1109\/CVPR42600.2020.01079"},{"issue":"21","key":"1532_CR41","doi-asserted-by":"publisher","first-page":"30685","DOI":"10.1007\/s11042-022-11940-1","volume":"81","author":"Y Luo","year":"2022","unstructured":"Luo, Y., Cao, X., Zhang, J., Guo, J., Shen, H., Wang, T., Feng, Q.: CE-FPN: enhancing channel information for object detection. Multimed Tools Appl 81(21), 30685\u201330704 (2022)","journal-title":"Multimed Tools Appl"},{"key":"1532_CR42","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1874\u20131883 (2016)","DOI":"10.1109\/CVPR.2016.207"},{"key":"1532_CR43","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 764\u2013773 (2017)","DOI":"10.1109\/ICCV.2017.89"},{"key":"1532_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.media.2017.06.015","volume":"42","author":"AAA Setio","year":"2017","unstructured":"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.: Validation, comparison, and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images: the luna16 challenge. Med. Image Anal. 42, 1\u201313 (2017)","journal-title":"Med. Image Anal."},{"key":"1532_CR45","doi-asserted-by":"publisher","first-page":"2139","DOI":"10.1007\/s00330-015-4030-7","volume":"26","author":"C Jacobs","year":"2016","unstructured":"Jacobs, C., Rikxoort, E.M., Murphy, K., Prokop, M., Schaefer-Prokop, C.M., Ginneken, B.: Computer-aided detection of pulmonary nodules: a comparative study using the public lidc\/idri database. Eur. Radiol. 26, 2139\u20132147 (2016)","journal-title":"Eur. Radiol."},{"key":"1532_CR46","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.neunet.2022.08.029","volume":"155","author":"K Min","year":"2022","unstructured":"Min, K., Lee, G.-H., Lee, S.-W.: Attentional feature pyramid network for small object detection. Neural Netw. 155, 439\u2013450 (2022)","journal-title":"Neural Netw."},{"issue":"8","key":"1532_CR47","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1121\/1.1908935","volume":"33","author":"JP Egan","year":"1961","unstructured":"Egan, J.P., Greenberg, G.Z., Schulman, A.I.: Operating characteristics, signal detectability, and the method of free response. J Acoust Soc Am 33(8), 993\u20131007 (1961)","journal-title":"J Acoust Soc Am"},{"key":"1532_CR48","doi-asserted-by":"crossref","unstructured":"Zhu, W., Liu, C., Fan, W., Xie, X.: Deeplung: Deep 3d dual path nets for automated pulmonary nodule detection and classification. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 673\u2013681 (2018). IEEE","DOI":"10.1109\/WACV.2018.00079"},{"key":"1532_CR49","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.patcog.2018.07.031","volume":"85","author":"H Xie","year":"2019","unstructured":"Xie, H., Yang, D., Sun, N., Chen, Z., Zhang, Y.: Automated pulmonary nodule detection in ct images using deep convolutional neural networks. Pattern Recogn. 85, 109\u2013119 (2019)","journal-title":"Pattern Recogn."},{"key":"1532_CR50","doi-asserted-by":"crossref","unstructured":"Li, Y., Fan, Y.: Deepseed: 3d squeeze-and-excitation encoder-decoder convolutional neural networks for pulmonary nodule detection. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1866\u20131869 (2020). IEEE","DOI":"10.1109\/ISBI45749.2020.9098317"},{"issue":"11","key":"1532_CR51","doi-asserted-by":"publisher","first-page":"5619","DOI":"10.1109\/JBHI.2022.3198509","volume":"26","author":"Z Zhou","year":"2022","unstructured":"Zhou, Z., Gou, F., Tan, Y., Wu, J.: A cascaded multi-stage framework for automatic detection and segmentation of pulmonary nodules in developing countries. IEEE J. Biomed. Health Inform. 26(11), 5619\u20135630 (2022)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"1532_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107748","volume":"241","author":"T-C Nguyen","year":"2023","unstructured":"Nguyen, T.-C., Nguyen, T.-P., Cao, T., Dao, T.T.P., Ho, T.-N., Nguyen, T.V., Tran, M.-T.: Manet: multi-branch attention auxiliary learning for lung nodule detection and segmentation. Comput. Methods Programs Biomed. 241, 107748 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"1532_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104875","volume":"85","author":"F Yousaf","year":"2023","unstructured":"Yousaf, F., Iqbal, S., Fatima, N., Kousar, T., Rahim, M.S.M.: Multi-class disease detection using deep learning and human brain medical imaging. Biomed. Signal Process. Control 85, 104875 (2023)","journal-title":"Biomed. Signal Process. Control"},{"key":"1532_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107398","volume":"231","author":"G Wang","year":"2023","unstructured":"Wang, G., Luo, X., Gu, R., Yang, S., Qu, Y., Zhai, S., Zhao, Q., Li, K., Zhang, S.: Pymic: a deep learning toolkit for annotation-efficient medical image segmentation. Comput. Methods Programs Biomed. 231, 107398 (2023)","journal-title":"Comput. Methods Programs Biomed."},{"key":"1532_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102656","volume":"83","author":"S Zhang","year":"2023","unstructured":"Zhang, S., Zhang, J., Tian, B., Lukasiewicz, T., Xu, Z.: Multi-modal contrastive mutual learning and pseudo-label re-learning for semi-supervised medical image segmentation. Med. Image Anal. 83, 102656 (2023)","journal-title":"Med. Image Anal."},{"key":"1532_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.104402","volume":"80","author":"W Li","year":"2023","unstructured":"Li, W., Zhang, Y., Wang, G., Huang, Y., Li, R.: Dfenet: a dual-branch feature enhanced network integrating transformers and convolutional feature learning for multimodal medical image fusion. Biomed. Signal Process. Control 80, 104402 (2023)","journal-title":"Biomed. Signal Process. Control"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01532-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-024-01532-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-024-01532-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T09:09:06Z","timestamp":1734340146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-024-01532-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":56,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["1532"],"URL":"https:\/\/doi.org\/10.1007\/s00530-024-01532-4","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"13 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}],"article-number":"328"}}