{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T06:16:35Z","timestamp":1778652995180,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T00:00:00Z","timestamp":1766275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"name":"The Research and Development Project of \u201cJianbing\u201d \u201cLingyan\u201d of Zhejiang Province","award":["2025C01136"],"award-info":[{"award-number":["2025C01136"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,21]]},"DOI":"10.1145\/3789938.3789957","type":"proceedings-article","created":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T05:59:12Z","timestamp":1778651952000},"page":"106-111","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ADE-CAN: An Asymmetric Dual-Encoder Cross-Attention Network for Lung Nodule Malignancy Prediction by Fusing Nodule and Peri-nodular Features"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4362-5166","authenticated-orcid":false,"given":"Xin","family":"Tan","sequence":"first","affiliation":[{"name":"College of Biomedical Engineering &amp; Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0022-2661","authenticated-orcid":false,"given":"Xuhua","family":"Huang","sequence":"additional","affiliation":[{"name":"First Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1570-8495","authenticated-orcid":false,"given":"Sheng","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering &amp; Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7658-5250","authenticated-orcid":false,"given":"Xudong","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Biomedical Engineering &amp; Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,12]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Chao Li Shaoyuan Lei Li Ding Yan Xu Xiaonan Wu Hui Wang Zijin Zhang Ting Gao Yongqiang Zhang and Lin Li. 2023. Global burden and trends of lung cancer incidence and mortality. Chinese medical journal 136 13 (2023) 1583\u20131590.","DOI":"10.1097\/CM9.0000000000002529"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Barnett\u00a0S Kramer Christine\u00a0D Berg Denise\u00a0R Aberle and Philip\u00a0C Prorok. 2011. Lung cancer screening with low-dose helical CT: results from the National Lung Screening Trial (NLST). 109\u2013111\u00a0pages.","DOI":"10.1258\/jms.2011.011055"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"Robert\u00a0J Gillies Paul\u00a0E Kinahan and Hedvig Hricak. 2016. Radiomics: images are more than pictures they are data. Radiology 278 2 (2016) 563\u2013577.","DOI":"10.1148\/radiol.2015151169"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Niha Beig Mohammadhadi Khorrami Mehdi Alilou Prateek Prasanna Nathaniel Braman Mahdi Orooji Sagar Rakshit Kaustav Bera Prabhakar Rajiah Jennifer Ginsberg et\u00a0al. 2019. Perinodular and intranodular radiomic features on lung CT images distinguish adenocarcinomas from granulomas. Radiology 290 3 (2019) 783\u2013792.","DOI":"10.1148\/radiol.2018180910"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Guixia Kang Kui Liu Beibei Hou and Ningbo Zhang. 2017. 3D multi-view convolutional neural networks for lung nodule classification. PloS one 12 11 (2017) e0188290.","DOI":"10.1371\/journal.pone.0188290"},{"key":"e_1_3_3_1_7_2","first-page":"244","volume-title":"2018 IEEE 31st international symposium on computer-based medical systems (CBMS)","author":"Da\u00a0N\u00f3brega Raul Victor\u00a0Medeiros","year":"2018","unstructured":"Raul Victor\u00a0Medeiros Da\u00a0N\u00f3brega, Solon\u00a0Alves Peixoto, Suane Pires\u00a0P da Silva, and Pedro\u00a0Pedrosa Rebou\u00e7as\u00a0Filho. 2018. Lung nodule classification via deep transfer learning in CT lung images. In 2018 IEEE 31st international symposium on computer-based medical systems (CBMS). IEEE, 244\u2013249."},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Hui Yu Jinqiu Li Lixin Zhang Yuzhen Cao Xuyao Yu and Jinglai Sun. 2021. Design of lung nodules segmentation and recognition algorithm based on deep learning. BMC bioinformatics 22 Suppl 5 (2021) 314.","DOI":"10.1186\/s12859-021-04234-0"},{"key":"e_1_3_3_1_9_2","first-page":"213","volume-title":"Proceedings of the 2024 6th International Conference on Image, Video and Signal Processing","author":"Kodera Shogo","year":"2024","unstructured":"Shogo Kodera, Wahyu Rahmaniar, Hiroko Oshibe, Ze Jin, Takeyuki Watadani, Osamu Abe, and Kenji Suzuki. 2024. Super-Efficient Lung Nodule Classification Using Massive-Training Artificial Neural Network (MTANN) Compact Model on LIDC-IDRI Database. In Proceedings of the 2024 6th International Conference on Image, Video and Signal Processing. 213\u2013220."},{"key":"e_1_3_3_1_10_2","first-page":"647","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Alilou Mehdi","year":"2017","unstructured":"Mehdi Alilou, Mahdi Orooji, and Anant Madabhushi. 2017. Intra-perinodular textural transition (ipris): A 3D descriptor for nodule diagnosis on lung CT. In International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 647\u2013655."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Ying Zeng Xiao Zhou Tianzhi Zhou Haibo Liu Yingjun Zhou Shanyue Lin and Wei Zhang. 2024. Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study. Journal of Cardiothoracic Surgery 19 1 (2024) 505.","DOI":"10.1186\/s13019-024-03008-y"},{"key":"e_1_3_3_1_12_2","first-page":"6450","volume-title":"Proceedings of the IEEE conference on Computer Vision and Pattern Recognition","author":"Tran Du","year":"2018","unstructured":"Du Tran, Heng Wang, Lorenzo Torresani, Jamie Ray, Yann LeCun, and Manohar Paluri. 2018. A closer look at spatiotemporal convolutions for action recognition. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 6450\u20136459."},{"key":"e_1_3_3_1_13_2","unstructured":"Fan Bai Yuxin Du Tiejun Huang Max Q-H Meng and Bo Zhao. 2024. M3d: Advancing 3d medical image analysis with multi-modal large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2404.00578 (2024)."},{"key":"e_1_3_3_1_14_2","unstructured":"Samuel\u00a0G Armato\u00a0III Geoffrey McLennan Luc Bidaut Michael\u00a0F McNitt-Gray Charles\u00a0R Meyer Anthony\u00a0P Reeves Binsheng Zhao Denise\u00a0R Aberle Claudia\u00a0I Henschke Eric\u00a0A Hoffman et\u00a0al. 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 2 (2011) 915\u2013931."},{"key":"e_1_3_3_1_15_2","unstructured":"Will Kay Joao Carreira Karen Simonyan Brian Zhang Chloe Hillier Sudheendra Vijayanarasimhan Fabio Viola Tim Green Trevor Back Paul Natsev et\u00a0al. 2017. The kinetics human action video dataset. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1705.06950 (2017)."},{"key":"e_1_3_3_1_16_2","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan\u00a0N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Xinzhuo Zhao Liyao Liu Shouliang Qi Yueyang Teng Jianhua Li and Wei Qian. 2018. Agile convolutional neural network for pulmonary nodule classification using CT images. International journal of computer assisted radiology and surgery 13 4 (2018) 585\u2013595.","DOI":"10.1007\/s11548-017-1696-0"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Ivan\u00a0William Harsono Suryadiputra Liawatimena and Tjeng\u00a0Wawan Cenggoro. 2022. Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning. Journal of King Saud University-Computer and Information Sciences 34 3 (2022) 567\u2013577.","DOI":"10.1016\/j.jksuci.2020.03.013"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"QingZeng Song Lei Zhao XingKe Luo and XueChen Dou. 2017. Using deep learning for classification of lung nodules on computed tomography images. Journal of healthcare engineering 2017 1 (2017) 8314740.","DOI":"10.1155\/2017\/8314740"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Aiden Nibali Zhen He and Dennis Wollersheim. 2017. Pulmonary nodule classification with deep residual networks. International journal of computer assisted radiology and surgery 12 10 (2017) 1799\u20131808.","DOI":"10.1007\/s11548-017-1605-6"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Lijing Sun Mengyi Zhang Yu Lu Wenjun Zhu Yang Yi and Fei Yan. 2024. Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning. Computers in Biology and Medicine 175 (2024) 108505.","DOI":"10.1016\/j.compbiomed.2024.108505"}],"event":{"name":"ICCBB 2025: 2025 9th International Conference on Computational Biology and Bioinformatics","location":"Tokyo Japan","acronym":"ICCBB 2025"},"container-title":["Proceedings of the 2025 9th International Conference on Computational Biology and Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3789938.3789957","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T06:04:10Z","timestamp":1778652250000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3789938.3789957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,21]]},"references-count":20,"alternative-id":["10.1145\/3789938.3789957","10.1145\/3789938"],"URL":"https:\/\/doi.org\/10.1145\/3789938.3789957","relation":{},"subject":[],"published":{"date-parts":[[2025,12,21]]},"assertion":[{"value":"2026-05-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}