{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T01:29:32Z","timestamp":1752283772903,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,12,20]],"date-time":"2019-12-20T00:00:00Z","timestamp":1576800000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"National Nature Science Fondation of China","award":["61806013"],"award-info":[{"award-number":["61806013"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,12,20]]},"DOI":"10.1145\/3377713.3377732","type":"proceedings-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T10:07:26Z","timestamp":1581070046000},"page":"104-111","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Review on Deep Learning Methods Used for Computer-aided Lung Cancer Detection and Diagnosis"],"prefix":"10.1145","author":[{"given":"Firdaous","family":"Essaf","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing University of Technology, Beijing, China"}]},{"given":"Yujian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin, Guangxi, China"}]},{"given":"Seybou","family":"Sakho","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing University of Technology, Beijing, China"}]},{"given":"Mesmin J. Mbyamm","family":"Kiki","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing University of Technology, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,2,7]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Trends in Cancer Death Rates for Selected Sites","author":"Facts C.","year":"2016","unstructured":"C. Facts , \"Joinpoint Trends in Cancer Incidence Rates for Selected Sites in Two Age Groups , US, 1995-2015 35 Figure S6. Trends in Cancer Death Rates for Selected Sites ,\" 2016 . C. Facts, \"Joinpoint Trends in Cancer Incidence Rates for Selected Sites in Two Age Groups, US, 1995-2015 35 Figure S6. Trends in Cancer Death Rates for Selected Sites,\" 2016."},{"key":"e_1_3_2_1_2_1","unstructured":"\"China National Cancer Center released the latest national report on the incidence and mortality rate of cancer Med Tech Industry in one space. Devices Finance Employment Conferences and News.\" [Online]. Available: http:\/\/www.medtechdirectory.com\/blog\/643-China-National-Cancer-Center-released-the-latest-national-report-on-the-incidence-and-mortality-rate-of-cancer.html. [Accessed: 27-Feb-2019].  \"China National Cancer Center released the latest national report on the incidence and mortality rate of cancer Med Tech Industry in one space. Devices Finance Employment Conferences and News.\" [Online]. Available: http:\/\/www.medtechdirectory.com\/blog\/643-China-National-Cancer-Center-released-the-latest-national-report-on-the-incidence-and-mortality-rate-of-cancer.html. [Accessed: 27-Feb-2019]."},{"key":"e_1_3_2_1_3_1","unstructured":"\"Cancer Statistics - National Cancer Institute.\" [Online]. Available: https:\/\/www.cancer.gov\/about-cancer\/understanding\/statistics. [Accessed: 14-Mar-2019].  \"Cancer Statistics - National Cancer Institute.\" [Online]. Available: https:\/\/www.cancer.gov\/about-cancer\/understanding\/statistics. [Accessed: 14-Mar-2019]."},{"key":"e_1_3_2_1_4_1","unstructured":"\"How Alibaba Cloud ET Medical Brain Is Transforming Healthcare with Artificial Intelligence - Alibaba Cloud Community.\" [Online]. Available: https:\/\/www.alibabacloud.com\/blog\/how-alibaba-cloud-et-medical-brain-is-transforming-healthcare-with-artificial-intelligence_593776. [Accessed: 28-Feb-2019].  \"How Alibaba Cloud ET Medical Brain Is Transforming Healthcare with Artificial Intelligence - Alibaba Cloud Community.\" [Online]. Available: https:\/\/www.alibabacloud.com\/blog\/how-alibaba-cloud-et-medical-brain-is-transforming-healthcare-with-artificial-intelligence_593776. [Accessed: 28-Feb-2019]."},{"key":"e_1_3_2_1_5_1","volume-title":"The Application of Artificial Intelligence at Chinese Digital Platform Giants: Baidu, Alibaba and Tencent,\" Ssrn, no","author":"Jia K.","year":"2018","unstructured":"K. Jia , M. Kenney , J. Mattila , and T. Seppala , \" The Application of Artificial Intelligence at Chinese Digital Platform Giants: Baidu, Alibaba and Tencent,\" Ssrn, no . February , 2018 . K. Jia, M. Kenney, J. Mattila, and T. Seppala, \"The Application of Artificial Intelligence at Chinese Digital Platform Giants: Baidu, Alibaba and Tencent,\" Ssrn, no. February, 2018."},{"key":"e_1_3_2_1_6_1","volume-title":"Lung Imaging and Computer Aided Diagnosis","author":"Ayman El-Baz J. S. S.","year":"2018","unstructured":"J. S. S. Ayman El-Baz , Lung Imaging and Computer Aided Diagnosis . 2018 . J. S. S. Ayman El-Baz, Lung Imaging and Computer Aided Diagnosis. 2018."},{"key":"e_1_3_2_1_7_1","unstructured":"\"Yitu Healthcare launches two cancer-related products - Chinadaily.com.cn.\" [Online]. Available: http:\/\/global.chinadaily.com.cn\/a\/201811\/29\/WS5bff5b03a310eff30328bc71.html. [Accessed: 28-Feb-2019].  \"Yitu Healthcare launches two cancer-related products - Chinadaily.com.cn.\" [Online]. Available: http:\/\/global.chinadaily.com.cn\/a\/201811\/29\/WS5bff5b03a310eff30328bc71.html. [Accessed: 28-Feb-2019]."},{"key":"e_1_3_2_1_8_1","first-page":"3","volume-title":"V. Kotov, L. Scholten, and N. Walasek, \"LUNA16 COMPETITION: FALSE POSITIVE REDUCTION","author":"De Bel T.","unstructured":"T. De Bel , C. Van Den Bogaard , V. Kotov, L. Scholten, and N. Walasek, \"LUNA16 COMPETITION: FALSE POSITIVE REDUCTION ( PROJECT REPORT : COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING) Data Science, ICIS Nijmegen ,\" vol. 52 , pp. 3 -- 5 . T. De Bel, C. Van Den Bogaard, V. Kotov, L. Scholten, and N. Walasek, \"LUNA16 COMPETITION: FALSE POSITIVE REDUCTION (PROJECT REPORT: COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING) Data Science, ICIS Nijmegen,\" vol. 52, pp. 3--5."},{"key":"e_1_3_2_1_9_1","unstructured":"C. Liu \"DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification.\"  C. Liu \"DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification.\""},{"key":"e_1_3_2_1_10_1","volume-title":"Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network","author":"Liao F.","year":"2017","unstructured":"F. Liao , M. Liang , Z. Li , X. Hu , and S. Song , \" Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network ,\" Nov. 2017 . F. Liao, M. Liang, Z. Li, X. Hu, and S. Song, \"Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-or Network,\" Nov. 2017."},{"key":"e_1_3_2_1_11_1","unstructured":"D. Hammack \"Forecasting Lung Cancer Diagnoses with Deep Learning \" pp. 1--6 2017.  D. Hammack \"Forecasting Lung Cancer Diagnoses with Deep Learning \" pp. 1--6 2017."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.06.015"},{"key":"e_1_3_2_1_13_1","unstructured":"M. Gao Z. Xu L. Lu A. P. Harrison R. M. Summers and D. J. Mollura \"Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Detection \" no. Cidi.  M. Gao Z. Xu L. Lu A. P. Harrison R. M. Summers and D. J. Mollura \"Multi-label Deep Regression and Unordered Pooling for Holistic Interstitial Lung Disease Detection \" no. Cidi."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2535865"},{"key":"e_1_3_2_1_15_1","unstructured":"\"LIDC-IDRI - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki.\" [Online]. Available: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/LIDC-IDRI. [Accessed: 20-Nov-2018].  \"LIDC-IDRI - The Cancer Imaging Archive (TCIA) Public Access - Cancer Imaging Archive Wiki.\" [Online]. Available: https:\/\/wiki.cancerimagingarchive.net\/display\/Public\/LIDC-IDRI. [Accessed: 20-Nov-2018]."},{"key":"e_1_3_2_1_16_1","first-page":"220","volume-title":"no. 03","author":"Patel S.","year":"2017","unstructured":"S. Patel , \" A Computer-Aided Diagnosis for Identification and Classification of Pulmonary Nodules Sheetal Patel,\" vol. 5 , no. 03 , pp. 220 -- 223 , 2017 . S. Patel, \"A Computer-Aided Diagnosis for Identification and Classification of Pulmonary Nodules Sheetal Patel,\" vol. 5, no. 03, pp. 220--223, 2017."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2012.02.022"},{"key":"e_1_3_2_1_18_1","volume-title":"Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge","author":"Kuan K.","year":"2017","unstructured":"K. Kuan , \" Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge ,\" 2017 . K. Kuan et al., \"Deep Learning for Lung Cancer Detection: Tackling the Kaggle Data Science Bowl 2017 Challenge,\" 2017."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3528204"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.2147\/CMAR.S174240"},{"key":"e_1_3_2_1_21_1","unstructured":"\"Hounsfield Scale - an overview | ScienceDirect Topics.\" [Online]. Available: https:\/\/www.sciencedirect.com\/topics\/medicine-and-dentistry\/hounsfield-scale. [Accessed: 02-Mar-2019].  \"Hounsfield Scale - an overview | ScienceDirect Topics.\" [Online]. Available: https:\/\/www.sciencedirect.com\/topics\/medicine-and-dentistry\/hounsfield-scale. [Accessed: 02-Mar-2019]."},{"key":"e_1_3_2_1_22_1","first-page":"2419","volume-title":"A Fast Automatic Method of Lung Segmentation in CT Images Using Mathematical Morphology,\" in World Congress on Medical Physics and Biomedical Engineering","author":"Li W.","year":"2006","unstructured":"W. Li , S. D. Nie , and J. J. Cheng , \" A Fast Automatic Method of Lung Segmentation in CT Images Using Mathematical Morphology,\" in World Congress on Medical Physics and Biomedical Engineering 2006 , Berlin, Heidelberg : Springer Berlin Heidelberg , 2007, pp. 2419 -- 2422 . W. Li, S. D. Nie, and J. J. Cheng, \"A Fast Automatic Method of Lung Segmentation in CT Images Using Mathematical Morphology,\" in World Congress on Medical Physics and Biomedical Engineering 2006, Berlin, Heidelberg: Springer Berlin Heidelberg, 2007, pp. 2419--2422."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2005.6.2.89"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2004.06.005"},{"volume-title":"A Generic Approach to Pathological Lung Segmentation","author":"Mansoor A.","key":"e_1_3_2_1_25_1","unstructured":"A. Mansoor , \" A Generic Approach to Pathological Lung Segmentation ,\" vol. 33 , no. 12, pp. 2293--2310, 2014. A. Mansoor et al., \"A Generic Approach to Pathological Lung Segmentation,\" vol. 33, no. 12, pp. 2293--2310, 2014."},{"key":"e_1_3_2_1_26_1","volume-title":"IEEE Int. Conf. Acoust. Speech Signal Process. - Proc.","volume":"1","author":"Shojaii R.","year":"2007","unstructured":"R. Shojaii , J. Alirezaie , and P. Babyn , \" Automatic segmentation of abnormal lung parenchyma utilizing wavelet transform,\" ICASSP , IEEE Int. Conf. Acoust. Speech Signal Process. - Proc. , vol. 1 , no. May , 2007 . R. Shojaii, J. Alirezaie, and P. Babyn, \"Automatic segmentation of abnormal lung parenchyma utilizing wavelet transform,\" ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 1, no. May, 2007."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2011.2171357"},{"key":"e_1_3_2_1_28_1","first-page":"989","volume-title":"Landmark-based segmentation of lungs while handling partial correspondences using sparse graph-based priors,\" in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","author":"Besbes A.","year":"2011","unstructured":"A. Besbes and N. Paragios , \" Landmark-based segmentation of lungs while handling partial correspondences using sparse graph-based priors,\" in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro , 2011 , pp. 989 -- 995 . A. Besbes and N. Paragios, \"Landmark-based segmentation of lungs while handling partial correspondences using sparse graph-based priors,\" in 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011, pp. 989--995."},{"key":"e_1_3_2_1_29_1","unstructured":"\"Medical Image Computing and Computer Assisted Intervention -- MICCAI 2017 ... - Google Books.\" [Online]. Available: https:\/\/books.google.com\/books?id=ZtszDwAAQBAJ&pg=PA168&lpg=PA168&dq=Multi-stage+learning+for+robust+lung+segmentation+in+challenging+CT+volumes%5BC%5D+\/\/+Proceedings+of+International+Conference+on+Medical+Image+Computing+and+Computer-Assisted+Interventio. [Accessed: 02-Mar-2019].  \"Medical Image Computing and Computer Assisted Intervention -- MICCAI 2017 ... - Google Books.\" [Online]. Available: https:\/\/books.google.com\/books?id=ZtszDwAAQBAJ&pg=PA168&lpg=PA168&dq=Multi-stage+learning+for+robust+lung+segmentation+in+challenging+CT+volumes%5BC%5D+\/\/+Proceedings+of+International+Conference+on+Medical+Image+Computing+and+Computer-Assisted+Interventio. [Accessed: 02-Mar-2019]."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4890597"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1166\/jctn.2015.4216"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2009.07.001"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2013.12.001"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4929562"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2010.05.005"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2016.2536809"},{"key":"e_1_3_2_1_37_1","volume-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick R.","year":"2013","unstructured":"R. Girshick , J. Donahue , T. Darrell , and J. Malik , \" Rich feature hierarchies for accurate object detection and semantic segmentation ,\" Nov. 2013 . R. Girshick, J. Donahue, T. Darrell, and J. Malik, \"Rich feature hierarchies for accurate object detection and semantic segmentation,\" Nov. 2013."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_2_1_40_1","unstructured":"M. F. Serj B. Lavi G. Hoff and D. P. Valls \"A Deep Convolutional Neural Network for Lung Cancer Diagnostic.\"  M. F. Serj B. Lavi G. Hoff and D. P. Valls \"A Deep Convolutional Neural Network for Lung Cancer Diagnostic.\""},{"key":"e_1_3_2_1_41_1","unstructured":"N. A. Order and A. L. L. A. With \"ZNET - LUNG NODULE DETECTION Moira Berens Robbert van der Gugten Michael de Kaste Jeroen Manders and Guido Zuidhof* \" pp. 3--6.  N. A. Order and A. L. L. A. With \"ZNET - LUNG NODULE DETECTION Moira Berens Robbert van der Gugten Michael de Kaste Jeroen Manders and Guido Zuidhof* \" pp. 3--6."},{"issue":"8","key":"e_1_3_2_1_42_1","article-title":"Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN)","volume":"8","author":"Alakwaa W.","year":"2017","unstructured":"W. Alakwaa , M. Nassef , and A. Badr , \" Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN) ,\" IJACSA) Int. J. Adv. Comput. Sci. Appl. , vol. 8 , no. 8 , 2017 . W. Alakwaa, M. Nassef, and A. Badr, \"Lung Cancer Detection and Classification with 3D Convolutional Neural Network (3D-CNN),\" IJACSA) Int. J. Adv. Comput. Sci. Appl., vol. 8, no. 8, 2017.","journal-title":"IJACSA) Int. J. Adv. Comput. Sci. Appl."},{"volume-title":"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?","author":"Tajbakhsh N.","key":"e_1_3_2_1_43_1","unstructured":"N. Tajbakhsh , \" Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning? ,\" vol. 35 , no. 5, pp. 1299--1312, 2017. N. Tajbakhsh et al., \"Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?,\" vol. 35, no. 5, pp. 1299--1312, 2017."},{"key":"e_1_3_2_1_44_1","first-page":"06651","article-title":"Deep Learning for the Classification of Lung Nodules,\" arXiv q-bio.","volume":"11","author":"Yang H.","year":"2016","unstructured":"H. Yang , H. Yu , and G. Wang , \" Deep Learning for the Classification of Lung Nodules,\" arXiv q-bio. QM , vol. 11 , p. 06651 , 2016 . H. Yang, H. Yu, and G. Wang, \"Deep Learning for the Classification of Lung Nodules,\" arXiv q-bio.QM, vol. 11, p. 06651, 2016.","journal-title":"QM"},{"key":"e_1_3_2_1_45_1","unstructured":"S. Ramaswamy and K. Truong \"Pulmonary Nodule Classification with Convolutional Neural Networks.\"  S. Ramaswamy and K. Truong \"Pulmonary Nodule Classification with Convolutional Neural Networks.\""},{"key":"e_1_3_2_1_46_1","volume-title":"Lung Nodule Detection using 3D Convolutional Neural Networks Trained on Weakly Labeled Data","author":"Anirudh R.","year":"2016","unstructured":"R. Anirudh , J. J. Thiagarajan , and P. T. Bremer , \" Lung Nodule Detection using 3D Convolutional Neural Networks Trained on Weakly Labeled Data ,\" 2016 . R. Anirudh, J. J. Thiagarajan, and P. T. Bremer, \"Lung Nodule Detection using 3D Convolutional Neural Networks Trained on Weakly Labeled Data,\" 2016."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2016.2613502"},{"key":"e_1_3_2_1_48_1","first-page":"379","volume-title":"Symp. Biomed. Imaging","author":"Huang X.","year":"2017","unstructured":"X. Huang , J. Shan , and V. Vaidya , \" Lung nodule detection in CT using 3D convolutional neural networks,\" Proc. - Int . Symp. Biomed. Imaging , pp. 379 -- 383 , 2017 . X. Huang, J. Shan, and V. Vaidya, \"Lung nodule detection in CT using 3D convolutional neural networks,\" Proc. - Int. Symp. Biomed. Imaging, pp. 379--383, 2017."},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.3109\/02841859909175574"},{"key":"e_1_3_2_1_50_1","unstructured":"J. Shi \"Lung Nodule Detection Using Convolutional Neural Networks \" 2018.  J. Shi \"Lung Nodule Detection Using Convolutional Neural Networks \" 2018."},{"key":"e_1_3_2_1_51_1","unstructured":"R. Gruetzemacher \"Using Deep Learning for Pulmonary Nodule Detection & Diagnosis \" pp. 1--7 2016.  R. Gruetzemacher \"Using Deep Learning for Pulmonary Nodule Detection & Diagnosis \" pp. 1--7 2016."},{"key":"e_1_3_2_1_52_1","volume-title":"Unsupervised Learning, and Deep Architectures","author":"Baldi P.","year":"2012","unstructured":"P. Baldi , \"Autoencoders , Unsupervised Learning, and Deep Architectures ,\" 2012 . P. Baldi, \"Autoencoders, Unsupervised Learning, and Deep Architectures,\" 2012."},{"key":"e_1_3_2_1_53_1","volume-title":"Learning Dynamics of Linear Denoising Autoencoders","author":"Pretorius A.","year":"2018","unstructured":"A. Pretorius , S. Kroon , and H. Kamper , \" Learning Dynamics of Linear Denoising Autoencoders ,\" 2018 . A. Pretorius, S. Kroon, and H. Kamper, \"Learning Dynamics of Linear Denoising Autoencoders,\" 2018."},{"key":"e_1_3_2_1_54_1","volume-title":"Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent Hugo Larochelle Yoshua Bengio Pierre-Antoine Manzagol","author":"Ca P. V.","year":"2010","unstructured":"P. V. Ca , L. T. Edu , I. Lajoie , Y. B. Ca , and P.-A. M. Ca , \"Stacked Denoising Autoencoders : Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent Hugo Larochelle Yoshua Bengio Pierre-Antoine Manzagol ,\" 2010 . P. V. Ca, L. T. Edu, I. Lajoie, Y. B. Ca, and P.-A. M. Ca, \"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion Pascal Vincent Hugo Larochelle Yoshua Bengio Pierre-Antoine Manzagol,\" 2010."},{"key":"e_1_3_2_1_55_1","unstructured":"D. Kumar A. Wong and D. A. Clausi \"Lung Nodule Classification Using Deep Features in CT Images.\"  D. Kumar A. Wong and D. A. Clausi \"Lung Nodule Classification Using Deep Features in CT Images.\""},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"B.-C. Kim Y. S. Sung and H.-I. Suk \"Deep feature learning for pulmonary nodule classification in a lung CT \" in 2016 4th International Winter Conference on Brain-Computer Interface (BCI) 2016 pp. 1--3.  B.-C. Kim Y. S. Sung and H.-I. Suk \"Deep feature learning for pulmonary nodule classification in a lung CT \" in 2016 4th International Winter Conference on Brain-Computer Interface (BCI) 2016 pp. 1--3.","DOI":"10.1109\/IWW-BCI.2016.7457462"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1034\/j.1600-0455.2003.00061.x"},{"key":"e_1_3_2_1_58_1","first-page":"41","volume-title":"26th Annu. Int. Conf. Mach. Learn.","volume":"2","author":"Bengio Y.","year":"2009","unstructured":"Y. Bengio , J. Louradour , R. Collobert , and J. Weston , \" Curriculum learning (tech report),\" Proc . 26th Annu. Int. Conf. Mach. Learn. , vol. 2 , no. 1, pp. 41 -- 48 , 2009 . Y. Bengio, J. Louradour, R. Collobert, and J. Weston, \"Curriculum learning (tech report),\" Proc. 26th Annu. Int. Conf. Mach. Learn., vol. 2, no. 1, pp. 41--48, 2009."},{"issue":"1","key":"e_1_3_2_1_59_1","first-page":"76","article-title":"Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis","volume":"21","author":"Ebner L.","year":"2016","unstructured":"L. Ebner , M. Anthimopoulos , A. Christe , S. Mougiakakou , and S. Christodoulidis , \" Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis ,\" IEEE J. Biomed. Heal. Informatics , vol. 21 , no. 1 , pp. 76 -- 84 , 2016 . L. Ebner, M. Anthimopoulos, A. Christe, S. Mougiakakou, and S. Christodoulidis, \"Multisource Transfer Learning With Convolutional Neural Networks for Lung Pattern Analysis,\" IEEE J. Biomed. Heal. Informatics, vol. 21, no. 1, pp. 76--84, 2016.","journal-title":"IEEE J. Biomed. Heal. Informatics"},{"key":"e_1_3_2_1_60_1","unstructured":"K. He X. Zhang S. Ren and J. Sun \"Deep Residual Learning for Image Recognition.\"  K. He X. Zhang S. Ren and J. Sun \"Deep Residual Learning for Image Recognition.\""}],"event":{"name":"ACAI 2019: 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence","sponsor":["Chinese Univ. of Hong Kong Chinese University of Hong Kong"],"location":"Sanya China","acronym":"ACAI 2019"},"container-title":["Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377713.3377732","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3377713.3377732","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:23:55Z","timestamp":1750202635000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3377713.3377732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,20]]},"references-count":60,"alternative-id":["10.1145\/3377713.3377732","10.1145\/3377713"],"URL":"https:\/\/doi.org\/10.1145\/3377713.3377732","relation":{},"subject":[],"published":{"date-parts":[[2019,12,20]]},"assertion":[{"value":"2020-02-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}