{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:08:03Z","timestamp":1788998883738,"version":"build-2803163510"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T00:00:00Z","timestamp":1781913600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T00:00:00Z","timestamp":1788998400000},"content-version":"vor","delay-in-days":82,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Early detection and precise fine-grained radiologic classification of lung cancer are crucial for formulating clinical treatment plans. However, existing computer-aided diagnosis (CAD) systems are mostly limited to binary nodule classification or treat radiologic feature classification and clinical TNM staging as isolated tasks. To address technical challenges such as drastic scale variations of tumors, subtle inter-class differences, and complex anatomical backgrounds, this paper proposes an end-to-end fine-grained lung cancer object detection algorithm based on an improved YOLO architecture. The proposed model accomplishes a complex 7-class detection task within a unified framework, encompassing normal tissue, three major non-small cell lung cancer (NSCLC) subtypes, and specific anatomical locations combined with TNM stages. Specifically, a Dilated Reparameterization Block (C3k2_DRB) is designed in the backbone to effectively enlarge the receptive field via multi-scale dilated convolutions, enhancing feature extraction for multi-scale tumors without introducing inference overhead. Meanwhile, an Explicit Positional Gating Optimization attention mechanism (C2PSA_EPGO) is introduced to adaptively focus on the most discriminative lesion textures and suppress irrelevant background noise. Furthermore, a dynamic upsampling strategy (DySample) is employed in the feature fusion stage to achieve content-aware spatial alignment, maximally preserving the morphological boundary details of the lesions. Experimental results on a complex lung cancer dataset comprising 1886 images demonstrate that the improved model achieves 97.6% Precision and 99.2% mean Average Precision (mAP@0.5) while maintaining extremely low parameters (2.46\u00a0M) and computational cost (6.3 GFLOPs). Compared with mainstream object detection algorithms, the proposed method exhibits significant advantages in localization precision and fine-grained classification performance, demonstrating the potential of the proposed architectural enhancements for complex, fine-grained medical vision tasks.<\/jats:p>","DOI":"10.1038\/s41598-026-58090-0","type":"journal-article","created":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T10:15:34Z","timestamp":1781950534000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fine-grained lung cancer object detection via dilated reparameterization and explicit positional gating optimization"],"prefix":"10.1038","volume":"16","author":[{"given":"Yitao","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqiong","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Liang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yizhe","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiyu","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"issue":"3","key":"58090_CR1","first-page":"209","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71(3), 209\u2013249 (2021).","journal-title":"CA Cancer J. Clin."},{"issue":"8","key":"58090_CR2","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Aerts, H. J. Artificial intelligence in radiology. Nat. Rev. Cancer 18(8), 500\u2013510 (2018).","journal-title":"Nat. Rev. Cancer"},{"issue":"1","key":"58090_CR3","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","volume":"4","author":"A Esteva","year":"2021","unstructured":"Esteva, A. et al. Deep learning-enabled medical computer vision. NPJ Digit. Med. 4(1), 5 (2021).","journal-title":"NPJ Digit. Med."},{"issue":"3","key":"58090_CR4","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1118\/1.4907961","volume":"42","author":"B Wang","year":"2015","unstructured":"Wang, B. et al. Pulmonary nodule detection in CT images based on shape constraint CV model. Med. Phys. 42(3), 1241\u20131254 (2015).","journal-title":"Med. Phys."},{"issue":"10","key":"58090_CR5","doi-asserted-by":"publisher","first-page":"1559","DOI":"10.1038\/s41591-018-0177-5","volume":"24","author":"N Coudray","year":"2018","unstructured":"Coudray, N. et al. Classification and mutation prediction from non\u2013small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559\u20131567 (2018).","journal-title":"Nat. Med."},{"key":"58090_CR6","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\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2117\u20132125 (2017).","DOI":"10.1109\/CVPR.2017.106"},{"key":"58090_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z. & Guo, B. Swin transformer: Hierarchical vision transformer using shifted windows. In\u00a0Proceedings of the IEEE\/CVF International Conference on Computer Vision\u00a010012\u201310022 (2021).","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"58090_CR8","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, Y., Ge, Y., Zhao, S., Song, L., Yue, X. & Shan, Y. Unireplknet: A universal perception large-kernel convnet for audio video point cloud time-series and image recognition. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u00a05513\u20135524 (2024).","DOI":"10.1109\/CVPR52733.2024.00527"},{"key":"58090_CR9","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In\u00a0International Conference on Medical Image Computing and Computer-Assisted Intervention\u00a0234\u2013241 (Cham: Springer international publishing, 2015).","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"1","key":"58090_CR10","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1148\/radiol.14132187","volume":"273","author":"HD Chae","year":"2014","unstructured":"Chae, H. D. et al. Computerized texture analysis of persistent part-solid ground-glass nodules: Differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology 273(1), 285\u2013293 (2014).","journal-title":"Radiology"},{"key":"58090_CR11","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J. Y. & Kweon, I. S. Cbam: Convolutional block attention module. In\u00a0Proceedings of the European Conference on Computer Vision (ECCV)\u00a03\u201319 (2018).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"58090_CR12","doi-asserted-by":"publisher","first-page":"107984","DOI":"10.52202\/079017-3429","volume":"37","author":"A Wang","year":"2024","unstructured":"Wang, A. et al. Yolov10: Real-time end-to-end object detection. Adv. Neural. Inf. Process. Syst. 37, 107984\u2013108011 (2024).","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"58090_CR13","doi-asserted-by":"crossref","unstructured":"Liu, W., Lu, H., Fu, H. & Cao, Z. Learning to upsample by learning to sample. In\u00a0Proceedings of the IEEE\/CVF International Conference on Computer Vision\u00a06027\u20136037 (2023).","DOI":"10.1109\/ICCV51070.2023.00554"},{"issue":"2","key":"58090_CR14","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato, S. G. III. et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011).","journal-title":"Med. Phys."},{"key":"58090_CR15","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. 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":"58090_CR16","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.ebiom.2017.12.026","volume":"27","author":"P Khosravi","year":"2018","unstructured":"Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O. & Hajirasouliha, I. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine 27, 317\u2013328 (2018).","journal-title":"EBioMedicine"},{"issue":"6","key":"58090_CR17","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","volume":"25","author":"D Ardila","year":"2019","unstructured":"Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25(6), 954\u2013961 (2019).","journal-title":"Nat. Med."},{"key":"58090_CR18","unstructured":"Ren, S., He, K., Girshick, R., & Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks.\u00a0Adv. Neural Inf. Process. Syst. 28 (2015)."},{"key":"58090_CR19","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You only look once: Unified, real-time object detection. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition\u00a0779\u2013788 (2016).","DOI":"10.1109\/CVPR.2016.91"},{"key":"58090_CR20","doi-asserted-by":"crossref","unstructured":"Wang, C. Y., Bochkovskiy, A. & Liao, H. Y. M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition\u00a07464\u20137475 (2023).","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"58090_CR21","doi-asserted-by":"crossref","unstructured":"Wang, C. Y., Yeh, I. H. & Mark Liao, H. Y. Yolov9: Learning what you want to learn using programmable gradient information. In\u00a0European Conference on Computer Vision 1\u201321 (Cham: Springer Nature Switzerland, 2024).","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"58090_CR22","doi-asserted-by":"crossref","unstructured":"Lin, T. Y., Goyal, P., Girshick, R., He, K. & Doll\u00e1r, P. Focal loss for dense object detection. In\u00a0Proceedings of the IEEE International Conference on Computer Vision 2980\u20132988 (2017).","DOI":"10.1109\/ICCV.2017.324"},{"key":"58090_CR23","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N. & Polosukhin, I. Attention is all you need.\u00a0Adv. Neural Inf. Process. Syst. 30 (2017)."},{"key":"58090_CR24","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In\u00a0Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 7132\u20137141 (2018).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"58090_CR25","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","volume":"53","author":"J Schlemper","year":"2019","unstructured":"Schlemper, J. et al. Attention gated networks: Learning to leverage salient regions in medical images. Med. Image Anal. 53, 197\u2013207 (2019).","journal-title":"Med. Image Anal."},{"key":"58090_CR26","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H. & Wei, Y. Deformable convolutional networks. In\u00a0Proceedings of the IEEE International Conference on Computer Vision 764\u2013773 (2017).","DOI":"10.1109\/ICCV.2017.89"},{"key":"58090_CR27","doi-asserted-by":"crossref","unstructured":"Ding, X., Zhang, X., Ma, N., Han, J., Ding, G. & Sun, J. Repvgg: Making vgg-style convnets great again. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 13733\u201313742 (2021).","DOI":"10.1109\/CVPR46437.2021.01352"},{"key":"58090_CR28","doi-asserted-by":"crossref","unstructured":"Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W. & Yeh, I. H. CSPNet: A new backbone that can enhance learning capability of CNN. In\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops 390\u2013391 (2020).","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"58090_CR29","unstructured":"Wang, C. Y., Liao, H. Y. M. & Yeh, I. H. Designing network design strategies through gradient path analysis. arXiv preprint arXiv:2211.04800. (2022)."},{"key":"58090_CR30","unstructured":"Jocher, G., Chaurasia, A., Qiu, J. & Stoken, A.\u00a0Ultralytics YOLOv8. GitHub repository. (2023)."},{"key":"58090_CR31","doi-asserted-by":"crossref","unstructured":"Tian, Y., Ye, Q. & Doermann, D. Yolov12: Attention-centric real-time object detectors.\u00a0arXiv preprint arXiv:2502.12524 (2025).","DOI":"10.52202\/085713-2627"},{"key":"58090_CR32","unstructured":"Khanam, R. & Hussain, M. Yolov11: An overview of the key architectural enhancements.\u00a0arXiv preprint arXiv:2410.17725 (2024)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58090-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58090-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58090-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T23:17:33Z","timestamp":1788995853000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58090-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,20]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["58090"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-58090-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,20]]},"assertion":[{"value":"9 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All methods in this study were carried out in accordance with relevant guidelines and regulations. This study exclusively utilized the publicly available, fully anonymized \u201cChest CT-Scan images Dataset\u201d from Kaggle. As this research strictly involves the secondary analysis of pre-existing, de-identified open-source data, it is exempt from the requirement for local Institutional Review Board (IRB) approval and patient informed consent.","order":2,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"28299"}}