{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T22:16:59Z","timestamp":1783117019592,"version":"3.54.6"},"reference-count":30,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901234"],"award-info":[{"award-number":["61901234"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20335"],"award-info":[{"award-number":["U23A20335"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62522315"],"award-info":[{"award-number":["62522315"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271315"],"award-info":[{"award-number":["42271315"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6202781"],"award-info":[{"award-number":["6202781"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62136004"],"award-info":[{"award-number":["62136004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2022A1515011418"],"award-info":[{"award-number":["2022A1515011418"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017610","name":"Shenzhen Science and Technology Innovation Program","doi-asserted-by":"publisher","award":["JCYJ20220530161409021"],"award-info":[{"award-number":["JCYJ20220530161409021"]}],"id":[{"id":"10.13039\/501100017610","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Efficient lung cancer screening relies on the automated and precise detection of pulmonary nodules in computed tomography (CT) images. Conventional anchor\u2010based methodologies often rely on pre\u2010defined anchor boxes, resulting in complex design and constrained adaptability to nodules of varying sizes. In contrast, anchor\u2010free models, characterized by their simpler design, have garnered increasing interest. However, existing anchor\u2010free methods may suffer from spatial misalignment across tasks and limited adaptability to diverse nodule shapes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study proposes a novel anchor\u2010free model, named DistAlignNet, which integrates distribution prediction and task\u2010aligned learning to address challenges such as spatial misalignment and the diversity of nodule\u00a0shapes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our model utilizes a one\u2010stage object detection architecture, enabling automatic learning of nodule shapes without explicit anchor box definitions. The model is optimized for both classification and localization tasks. In the localization task, we incorporate distribution prediction to improve noise robustness and adapt to the distinctive shape patterns of each nodule. In addition, we introduce a task\u2010aligned learning strategy to mitigate inconsistencies between different tasks and dynamically adjust the label assignment\u00a0process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental evaluations were performed on the publicly available LUNA16 dataset, where our model achieved an average Competition performance metric (CPM) score of 0.924. Compared with the SCPM\u2010Net model that reported the strongest performance among the existing approaches, the statistical analysis yielded  and , with a Cohen's  of 1.582, reflecting a statistically significant improvement. These results show that our model achieves superior performance compared with state\u2010of\u2010the\u2010art anchor\u2010based and anchor\u2010free methods for pulmonary nodule\u00a0detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Quantitative results and qualitative assessments demonstrate the effectiveness of our model, indicating its potential for clinical deployment in pulmonary nodule\u00a0detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70438","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T09:04:27Z","timestamp":1776330267000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhanced anchor\u2010free pulmonary nodule detection utilizing distribution prediction and task\u2010aligned learning strategies"],"prefix":"10.1002","volume":"53","author":[{"given":"Ling","family":"Ma","sequence":"first","affiliation":[{"name":"College of Software Nankai University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichen","family":"Ji","sequence":"additional","affiliation":[{"name":"College of Software Nankai University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Software Nankai University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingyu","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Software Nankai University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Software Nankai University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Wei","sequence":"additional","affiliation":[{"name":"Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China"},{"name":"School of Automation Northwestern Polytechnical University  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lizhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology Sun Yat\u2010Sen University Cancer Center  Guangzhou Guangdong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijie","family":"Zhao","sequence":"additional","affiliation":[{"name":"Research and Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen China"},{"name":"School of Automation Northwestern Polytechnical University  Xi'an Shaanxi China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-023-00798-3"},{"issue":"1","key":"e_1_2_8_3_1","article-title":"Diagnostic value of artificial intelligence based on CT image in benign and malignant pulmonary nodules","volume":"2022","author":"Du W","year":"2022","journal-title":"Int J Oncol"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"issue":"6","key":"e_1_2_8_5_1","first-page":"1137","article-title":"Faster R\u2010CNN: towards real\u2010time object detection with region proposal networks","volume":"39","author":"Ren S","year":"2017","journal-title":"IEEE Trans Power Appar Syst"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"LiuW AnguelovD ErhanD et\u00a0al.Ssd: single shot multibox detector. In:Computer Vision\u2010ECCV 2016: 14th European Conference Amsterdam The Netherlands October 11\u201014 2016 Proceedings Part I 14Springer.2016:21\u201037.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"e_1_2_8_7_1","unstructured":"LinTY GoyalP GirshickR HeK Doll\u00e1rP.Focal loss for dense object detection. In:Proceedings of the IEEE International Conference on Computer Vision.2017:2980\u20102988."},{"key":"e_1_2_8_8_1","doi-asserted-by":"crossref","unstructured":"ZhuW LiuC FanW XieX.Deeplung: deep 3d dual path nets for automated pulmonary nodule detection and classification. In:2018 IEEE Winter Conference on Applications of computer vision (WACV).IEEE.2018:673\u2010681.","DOI":"10.1109\/WACV.2018.00079"},{"key":"e_1_2_8_9_1","doi-asserted-by":"crossref","unstructured":"LiY FanY.DeepSEED: 3D squeeze\u2010and\u2010excitation encoder\u2010decoder convolutional neural networks for pulmonary nodule detection. In:2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI).IEEE.2020:1866\u20101869.","DOI":"10.1109\/ISBI45749.2020.9098317"},{"key":"e_1_2_8_10_1","article-title":"SANet: a slice\u2010aware network for pulmonary nodule detection","author":"Mei J","year":"2021","journal-title":"IEEE Trans Power Appar Syst"},{"key":"e_1_2_8_11_1","article-title":"Objects as points","author":"Zhou X","year":"2019","journal-title":"arXiv preprint arXiv:1904.07850"},{"key":"e_1_2_8_12_1","doi-asserted-by":"crossref","unstructured":"TianZ ShenC ChenH HeT.Fcos: fully convolutional one\u2010stage object detection. In:Proceedings of the IEEE\/CVF International Conference on Computer Vision.2019:9627\u20109636.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"e_1_2_8_13_1","doi-asserted-by":"crossref","unstructured":"CarionN MassaF SynnaeveG UsunierN KirillovA ZagoruykoS.End\u2010to\u2010end object detection with transformers. In:European Conference on Computer Vision.Springer.2020:213\u2010229.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_2_8_14_1","doi-asserted-by":"crossref","unstructured":"SunP ZhangR JiangY et\u00a0al.Sparse r\u2010cnn: end\u2010to\u2010end object detection with learnable proposals. In:Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2021:14454\u201014463.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"SongW TangF MarshallH FongKM LiuF.An improved anchor\u2010free nodule detection system using feature pyramid network. In:2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).IEEE.2023:1\u20104.","DOI":"10.1109\/EMBC40787.2023.10340341"},{"key":"e_1_2_8_16_1","doi-asserted-by":"crossref","unstructured":"SongT ChenJ LuoX et\u00a0al.CPM\u2010Net: a 3D center\u2010points matching network for pulmonary nodule detection in CT scans. In:International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.Springer.2020:550\u2010559.","DOI":"10.1007\/978-3-030-59725-2_53"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102287"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"FengC ZhongY GaoY ScottMR HuangW.Tood: Task\u2010aligned one\u2010stage object detection. In:2021 IEEE\/CVF International Conference on Computer Vision (ICCV)IEEE Computer Society.2021:3490\u20103499.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3223213"},{"key":"e_1_2_8_20_1","first-page":"21002","article-title":"Generalized focal loss: learning qualified and distributed bounding boxes for dense object detection","volume":"33","author":"Li X","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/6.1.1"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781003456285"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/34.1-2.28"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.4324\/9780203771587"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1111\/opo.12131"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10654-016-0149-3"},{"key":"e_1_2_8_27_1","unstructured":"IBM Corp N.IBM SPSS statistics for windows.2017."},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2020.03.013"},{"key":"e_1_2_8_29_1","doi-asserted-by":"crossref","unstructured":"KhosravanN BagciU.S4ND: single\u2010shot single\u2010scale lung nodule detection. In:International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.Springer.2018:794\u2010802.","DOI":"10.1007\/978-3-030-00934-2_88"},{"key":"e_1_2_8_30_1","doi-asserted-by":"crossref","unstructured":"TangH ZhangC XieX.Nodulenet: decoupled false positive reduction for pulmonary nodule detection and segmentation. In:International Conference on Medical Image Computing and Computer\u2010Assisted Intervention.Springer.2019:266\u2010274.","DOI":"10.1007\/978-3-030-32226-7_30"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.104866"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70438","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.70438","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.70438","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T21:26:30Z","timestamp":1783113990000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.70438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["10.1002\/mp.70438"],"URL":"https:\/\/doi.org\/10.1002\/mp.70438","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"2024-11-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-19","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70438"}}