{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T21:04:58Z","timestamp":1761253498444,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,20]],"date-time":"2020-10-20T00:00:00Z","timestamp":1603152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Zhejiang Science and Technology Project","award":["LGG18F010004"],"award-info":[{"award-number":["LGG18F010004"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,20]]},"DOI":"10.1145\/3424978.3425094","type":"proceedings-article","created":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T19:06:17Z","timestamp":1602788777000},"page":"1-5","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification of Lung Nodules Based on GAN and 3D CNN"],"prefix":"10.1145","author":[{"given":"Bin","family":"Sun","sequence":"first","affiliation":[{"name":"College of Information &amp; Electronic Engineering, Zhejiang University, Hangzhou, China"}]},{"given":"Fengyin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information &amp; Electronic Engineering, Zhejiang University, Hangzhou, China"}]},{"given":"Yusun","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Information &amp; Electronic Engineering, Zhejiang University City College, Hangzhou, China"}]},{"given":"Shaolei","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Information &amp; Electronic Engineering, Zhejiang University, Hangzhou, China"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information &amp; Electronic Engineering, Zhejiang University, Hangzhou, China"}]},{"given":"Xinyu","family":"Jin","sequence":"additional","affiliation":[{"name":"College of Information &amp; Electronic Engineering, Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2020,10,20]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.21037\/jtd.2019.08.12"},{"issue":"18","key":"e_1_3_2_1_2_1","first-page":"39","article-title":"CT Diagnosis and Differential Diagnosis of Solitary Pulmonary Nodules. Chinese Journal of CT and MRI","volume":"8","author":"Hu Lei","year":"2020","unstructured":"Lei Hu and Zhiliang Wang ( 2020 ). CT Diagnosis and Differential Diagnosis of Solitary Pulmonary Nodules. Chinese Journal of CT and MRI , AUG , 8 ( 18 ), 39 -- 42 . Lei Hu and Zhiliang Wang (2020). CT Diagnosis and Differential Diagnosis of Solitary Pulmonary Nodules. Chinese Journal of CT and MRI, AUG, 8(18), 39--42.","journal-title":"AUG"},{"key":"e_1_3_2_1_3_1","volume-title":"Application of Generative adversarial Nets in medical image processing. Life science instruments, 16(Z1), 71--80+91","author":"Chen Kun","year":"2018","unstructured":"Kun Chen and Qin Qiao ( 2018 ). Application of Generative adversarial Nets in medical image processing. Life science instruments, 16(Z1), 71--80+91 . Kun Chen and Qin Qiao (2018). Application of Generative adversarial Nets in medical image processing. Life science instruments, 16(Z1), 71--80+91."},{"issue":"6","key":"e_1_3_2_1_4_1","first-page":"1060","article-title":"Review of Deep Learning Methods for Detection and Classification of Pulmonary Nodules","volume":"36","author":"Zhao Qingyi","year":"2019","unstructured":"Qingyi Zhao and Ping Kong ( 2019 ). Review of Deep Learning Methods for Detection and Classification of Pulmonary Nodules . Journal of Biomedical Engineering , 36 ( 6 ), 1060 -- 1068 . Qingyi Zhao and Ping Kong (2019). Review of Deep Learning Methods for Detection and Classification of Pulmonary Nodules. Journal of Biomedical Engineering, 36(6), 1060--1068.","journal-title":"Journal of Biomedical Engineering"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2536809"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_2_1_7_1","volume-title":"Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks, https:\/\/arxiv.org\/abs\/1706.04303","author":"Ding Jia","year":"2017","unstructured":"Jia Ding and Aoxue Li ( 2017 ). Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks, https:\/\/arxiv.org\/abs\/1706.04303 . Jia Ding and Aoxue Li (2017). Accurate Pulmonary Nodule Detection in Computed Tomography Images Using Deep Convolutional Neural Networks, https:\/\/arxiv.org\/abs\/1706.04303."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2018.2879449"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2613502"},{"issue":"3","key":"e_1_3_2_1_10_1","first-page":"423","article-title":"False Positive Reduction of Pulmonary Nodules Using 3D CNN","volume":"40","author":"You Kun","year":"2019","unstructured":"Kun You and Pengyi Hao ( 2019 ). False Positive Reduction of Pulmonary Nodules Using 3D CNN . Journal of Graphics , 40 ( 3 ), 423 -- 428 . Kun You and Pengyi Hao (2019). False Positive Reduction of Pulmonary Nodules Using 3D CNN. Journal of Graphics, 40(3), 423--428.","journal-title":"Journal of Graphics"},{"issue":"10","key":"e_1_3_2_1_11_1","first-page":"93","article-title":"Progress and Prospects of Medical Images Synthesis Based on Generative Adversarial Networks","volume":"40","author":"Zhang Jie","year":"2019","unstructured":"Jie Zhang and Huijun Zhao ( 2019 ). Progress and Prospects of Medical Images Synthesis Based on Generative Adversarial Networks . Chinese Medical Equipment Journal , 40 ( 10 ), 93 -- 98 . Jie Zhang and Huijun Zhao (2019). Progress and Prospects of Medical Images Synthesis Based on Generative Adversarial Networks. Chinese Medical Equipment Journal, 40(10), 93--98.","journal-title":"Chinese Medical Equipment Journal"},{"issue":"6","key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","first-page":"256","DOI":"10.3788\/AOS201939.0615006","article-title":"Pulmonary Nodule Recognition Bases on Three-dimensional Convolution Neural Network","volume":"39","author":"Feng Yu","year":"2019","unstructured":"Yu Feng and Benshun Yi ( 2019 ). Pulmonary Nodule Recognition Bases on Three-dimensional Convolution Neural Network . Acta Optica Sinica , 39 ( 6 ), 256 -- 261 . Yu Feng and Benshun Yi (2019). Pulmonary Nodule Recognition Bases on Three-dimensional Convolution Neural Network. Acta Optica Sinica, 39(6), 256--261.","journal-title":"Acta Optica Sinica"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3528204"},{"key":"e_1_3_2_1_14_1","volume-title":"Self-supervised Transfer Learning Based on Partial Annotated CT Images for Pulmonary Nodule Classification","author":"Huang Hong","year":"2020","unstructured":"Hong Huang and Chao Peng ( 2020 )., Self-supervised Transfer Learning Based on Partial Annotated CT Images for Pulmonary Nodule Classification , http:\/\/kns.cnki.net\/kcms\/detail\/31.1252.O4.20200706.1125.002.html. Hong Huang and Chao Peng (2020)., Self-supervised Transfer Learning Based on Partial Annotated CT Images for Pulmonary Nodule Classification, http:\/\/kns.cnki.net\/kcms\/detail\/31.1252.O4.20200706.1125.002.html."},{"key":"e_1_3_2_1_15_1","volume-title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, https:\/\/arxiv.org\/abs\/1511.06434","author":"Alec Radford","year":"2015","unstructured":"Radford Alec and Metz Luke ( 2015 ). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, https:\/\/arxiv.org\/abs\/1511.06434 . Radford Alec and Metz Luke (2015). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, https:\/\/arxiv.org\/abs\/1511.06434."},{"key":"e_1_3_2_1_16_1","volume-title":"ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems","author":"Alex Krizhevsky","year":"2012","unstructured":"Krizhevsky Alex and Sutskever Ilya ( 2012 ). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems , http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf. Krizhevsky Alex and Sutskever Ilya (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems, http:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf."},{"issue":"12","key":"e_1_3_2_1_17_1","first-page":"1474","article-title":"Review of Image Classification Algorithms Based on Convolutional Neural Networks","volume":"34","author":"Yang Zhenzhen","year":"2018","unstructured":"Zhenzhen Yang and Nan Kuang ( 2018 ). Review of Image Classification Algorithms Based on Convolutional Neural Networks . Journal of Signal Processing , 34 ( 12 ), 1474 -- 1489 . Zhenzhen Yang and Nan Kuang (2018). Review of Image Classification Algorithms Based on Convolutional Neural Networks. Journal of Signal Processing, 34(12), 1474--1489.","journal-title":"Journal of Signal Processing"},{"key":"e_1_3_2_1_18_1","volume-title":"Research on Image Classification Training Method Based on Transfer Learning. China Computer & Communication (theoretical edition), 32(7), 53--55","author":"Xie Xiaohong","year":"2020","unstructured":"Xiaohong Xie and Wentao Li ( 2020 ). Research on Image Classification Training Method Based on Transfer Learning. China Computer & Communication (theoretical edition), 32(7), 53--55 . Xiaohong Xie and Wentao Li (2020). Research on Image Classification Training Method Based on Transfer Learning. China Computer & Communication (theoretical edition), 32(7), 53--55."}],"event":{"name":"CSAE 2020: The 4th International Conference on Computer Science and Application Engineering","acronym":"CSAE 2020","location":"Sanya China"},"container-title":["Proceedings of the 4th International Conference on Computer Science and Application Engineering"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3424978.3425094","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3424978.3425094","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:16Z","timestamp":1750197736000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3424978.3425094"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,20]]},"references-count":18,"alternative-id":["10.1145\/3424978.3425094","10.1145\/3424978"],"URL":"https:\/\/doi.org\/10.1145\/3424978.3425094","relation":{},"subject":[],"published":{"date-parts":[[2020,10,20]]},"assertion":[{"value":"2020-10-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}