{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:38:26Z","timestamp":1780357106339,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T00:00:00Z","timestamp":1579651200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T00:00:00Z","timestamp":1579651200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"The Program for Professor of Special Appointment","award":["ES2012XX"],"award-info":[{"award-number":["ES2012XX"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2020,4]]},"DOI":"10.1007\/s11517-019-02111-w","type":"journal-article","created":{"date-parts":[[2020,1,22]],"date-time":"2020-01-22T03:02:30Z","timestamp":1579662150000},"page":"725-737","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["A deep convolutional neural network architecture for interstitial lung disease pattern classification"],"prefix":"10.1007","volume":"58","author":[{"given":"Sheng","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Miao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Si","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaowen","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,1,22]]},"reference":[{"key":"2111_CR1","doi-asserted-by":"crossref","unstructured":"Altaf F, Islam S, Akhtar N, Janjua NK (2019) Going deep in medical image analysis: Concepts, methods, challenges and future directions. arXiv:1902.05655","DOI":"10.1109\/ACCESS.2019.2929365"},{"issue":"5","key":"2111_CR2","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","volume":"35","author":"M Anthimopoulos","year":"2016","unstructured":"Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S (2016) Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imag 35 (5):1207\u20131216","journal-title":"IEEE Trans Med Imag"},{"issue":"6","key":"2111_CR3","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1136\/thx.2003.020396","volume":"59","author":"ZA Aziz","year":"2004","unstructured":"Aziz ZA, Wells AU, Hansell DM, Bain G, Copley SJ, Desai SR, Ellis SM, Gleeson FV, Grubnic S, Nicholson AG, et al. (2004) Hrct diagnosis of diffuse parenchymal lung disease: inter-observer variation. Thorax 59(6):506\u2013511","journal-title":"Thorax"},{"issue":"9","key":"2111_CR4","doi-asserted-by":"publisher","first-page":"1790","DOI":"10.1109\/TPAMI.2015.2500224","volume":"38","author":"H Azizpour","year":"2016","unstructured":"Azizpour H, Razavian AS, Sullivan J, Maki A, Carlsson S (2016) Factors of transferability for a generic ConvNet representation. IEEE Trans Pattern Anal Mach Intell 38(9):1790\u20131802","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2111_CR5","doi-asserted-by":"crossref","unstructured":"Chen G, Zhang J, Zhuo D, Pan Y, Pang C (2019) Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. Medical & Biological Engineering & Computing:1\u201314","DOI":"10.1007\/s11517-019-01976-1"},{"issue":"5","key":"2111_CR6","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.1109\/JBHI.2017.2769800","volume":"22","author":"V Cheplygina","year":"2018","unstructured":"Cheplygina V, Pena IP, Pedersen JH, Lynch DA, S\u00f8rensen L., de Bruijne M (2018) Transfer learning for multicenter classification of chronic obstructive pulmonary disease. IEEE J Biomed Health Informat 22 (5):1486\u20131496","journal-title":"IEEE J Biomed Health Informat"},{"issue":"1","key":"2111_CR7","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/JBHI.2016.2636929","volume":"21","author":"S Christodoulidis","year":"2017","unstructured":"Christodoulidis S, Anthimopoulos M, Ebner L, Christe A, Mougiakakou S (2017) Multisource transfer learning with convolutional neural networks for lung pattern analysis. IEEE J Biomed Health Informat 21(1):76\u201384","journal-title":"IEEE J Biomed Health Informat"},{"key":"2111_CR8","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. Proc IEEE Conf Comput Vis Pattern Recognit","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"3","key":"2111_CR9","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.compmedimag.2011.07.003","volume":"36","author":"A Depeursinge","year":"2012","unstructured":"Depeursinge A, Vargas A, Platon A, Geissbuhler A, Poletti PA, M\u00fcller H. (2012) Building a reference multimedia database for interstitial lung diseases. Comput Med Imag Graph 36(3):227\u2013238","journal-title":"Comput Med Imag Graph"},{"key":"2111_CR10","unstructured":"Gao M, Bagci U, Lu L, Wu A, Buty M, Shin HC, Roth H, Papadakis GZ, Depeursinge A, Summers RM (2015) Holistic classification of ct attenuation patterns for interstitial lung diseases via deep convolutional neural networks. 1st Workshop Deep Learn. Med. Image Anal:41\u201348"},{"key":"2111_CR11","unstructured":"Guo W, Xu Z, Zhang H (2018) Interstitial lung disease classification using improved densenet. Multimed Tools Appl:1\u201312"},{"key":"2111_CR12","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"5786","key":"2111_CR13","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013507","journal-title":"Science"},{"key":"2111_CR14","unstructured":"Hjelm RD, Fedorov A, Lavoie-Marchildon S, Grewal K, Trischler A, Bengio Y (2018) Learning deep representations by mutual information estimation and maximization. arXiv:1808.06670"},{"key":"2111_CR15","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 4700\u20134708","DOI":"10.1109\/CVPR.2017.243"},{"key":"2111_CR16","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:1412.6980"},{"key":"2111_CR17","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proc Adv Neural Inf Process Syst, pp 1097\u20131105"},{"key":"2111_CR18","unstructured":"Kylberg G (2011) The Kylberg texture dataset v. 1.0, centre for image analysis, Swedish University of Agricultural Sciences and Uppsala University, external report (blue series) no 35"},{"issue":"11","key":"2111_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P, et al. (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"2111_CR20","doi-asserted-by":"crossref","unstructured":"Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014) Medical image classification with convolutional neural network. In: Proc 13th Int Conf Control Automat Robot Vis. IEEE, pp 844\u2013848","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"2111_CR21","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, S\u00e1nchez CI (2017) A survey on deep learning in medical image analysis. Med Imag Anal 42:60\u201388","journal-title":"Med Imag Anal"},{"key":"2111_CR22","unstructured":"Lu Y, Chen L, Saidi A (2017) Optimal transport for deep joint transfer learning. arXiv:1709.02995"},{"key":"2111_CR23","doi-asserted-by":"crossref","unstructured":"O\u2019Neil A, Shepherd M, Beveridge E, Goatman K (2017) A comparison of texture features versus deep learning for image classification in interstitial lung disease. In: Proc Ann Conf Med Imag Und Anal. Springer, pp 743\u2013753","DOI":"10.1007\/978-3-319-60964-5_65"},{"issue":"10","key":"2111_CR24","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"2111_CR25","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s11517-018-1819-y","volume":"57","author":"S Pang","year":"2019","unstructured":"Pang S, Du A, Orgun MA, Yu Z (2019) A novel fused convolutional neural network for biomedical image classification. Medical & Biological Engineering & Computing 57(1):107\u2013121","journal-title":"Medical & Biological Engineering & Computing"},{"key":"2111_CR26","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch"},{"key":"2111_CR27","doi-asserted-by":"crossref","unstructured":"Samala RK, Chan HP, Hadjiiski L, Helvie MA, Richter CD, Cha KH (2018) Breast cancer diagnosis in digital breast tomosynthesis: effects of training sample size on multi-stage transfer learning using deep neural nets. IEEE Trans Med Imag","DOI":"10.1109\/TMI.2018.2870343"},{"key":"2111_CR28","unstructured":"Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S (2014) Cnn features off-the-shelf: an astounding baseline for recognition. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp. 806\u2013813"},{"issue":"5","key":"2111_CR29","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imag 35(5):1285\u20131298","journal-title":"IEEE Trans Med Imag"},{"key":"2111_CR30","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"issue":"4","key":"2111_CR31","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1109\/TMI.2005.862753","volume":"25","author":"I Sluimer","year":"2006","unstructured":"Sluimer I, Schilham A, Prokop M, Van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imag 25(4):385\u2013405","journal-title":"IEEE Trans Med Imag"},{"issue":"Suppl 1","key":"2111_CR32","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1136\/thx.54.suppl_1.S1","volume":"54","author":"B Society","year":"1999","unstructured":"Society B, Committee S (1999) The diagnosis, assessment and treatment of diffuse parenchymal lung disease in adults. Thorax 54(Suppl 1):S1\u2013S28","journal-title":"Thorax"},{"key":"2111_CR33","unstructured":"Suzuki A, Sakanashi H, Kido S, Shouno H (2018) Feature representation analysis of deep convolutional neural network using two-stage feature transfer-an application for diffuse lung disease classification. arXiv:1810.06282"},{"key":"2111_CR34","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proc IEEE Conf Comput Vis Pattern Recognit, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"5","key":"2111_CR35","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","volume":"35","author":"N Tajbakhsh","year":"2016","unstructured":"Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Liang J (2016) Convolutional neural networks for medical image analysis: full training or fine tuning. IEEE Trans Med Imag 35 (5):1299\u20131312","journal-title":"IEEE Trans Med Imag"},{"key":"2111_CR36","doi-asserted-by":"crossref","unstructured":"Tan B, Zhang Y, Pan SJ, Yang Q (2017) Distant domain transfer learning. In: Proc. 31th AAAI Conf Artif Intell","DOI":"10.1609\/aaai.v31i1.10826"},{"key":"2111_CR37","doi-asserted-by":"crossref","unstructured":"Tarando SR, Fetita C, Faccinetto A, Brillet PY (2016) Increasing cad system efficacy for lung texture analysis using a convolutional network. In: Medical imaging 2016: Computer-aided diagnosis, vol 9785, p 97850Q","DOI":"10.1117\/12.2217752"},{"issue":"6","key":"2111_CR38","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/j.media.2010.05.005","volume":"14","author":"B Van Ginneken","year":"2010","unstructured":"Van Ginneken B, Armato S.G III, de Hoop B, van Amelsvoortvan de Vorst S, Duindam T, Niemeijer M, Murphy K, Schilham A, Retico A, Fantacci ME, et al. (2010) Comparing and combining algorithms for computer-aided detection of pulmonary nodules in computed tomography scans: the ANODE09 study. Med Imag Anal 14(6):707\u2013722","journal-title":"Med Imag Anal"},{"issue":"8","key":"2111_CR39","doi-asserted-by":"publisher","first-page":"3802","DOI":"10.1166\/jctn.2017.6676","volume":"14","author":"X Wei","year":"2017","unstructured":"Wei X, Chen J, Cai C (2017) Using deep convolutional neural networks and transfer learning for mammography mass lesion classification. J Comput Theor Nanos 14(8):3802\u20133806","journal-title":"J Comput Theor Nanos"},{"issue":"4","key":"2111_CR40","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","volume":"22","author":"MH Yap","year":"2018","unstructured":"Yap MH, Pons G, Mart\u00ed J, Ganau S, Sent\u00eds M, Zwiggelaar R, Davison AK, Mart\u00ed R (2018) Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J Biomed Health Informat 22(4):1218\u20131226","journal-title":"IEEE J Biomed Health Informat"},{"key":"2111_CR41","unstructured":"Yosinski J, Clune J, Bengio Y, Lipson H (2014) How transferable are features in deep neural networks?. In: Proc Adv Neural Inf Process Syst, pp 3320\u20133328"},{"key":"2111_CR42","unstructured":"Zheng L, Zhao Y, Wang S, Wang J, Tian Q (2016) Good practice in CNN feature transfer. arXiv:1604.00133"},{"key":"2111_CR43","unstructured":"Zhuang F, Cheng X, Luo P, Pan SJ, He Q (2015) Supervised representation learning: transfer learning with deep autoencoders. In: Proc 24th Int Conf Artif Intell"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-019-02111-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11517-019-02111-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-019-02111-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T07:30:30Z","timestamp":1665559830000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11517-019-02111-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,22]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,4]]}},"alternative-id":["2111"],"URL":"https:\/\/doi.org\/10.1007\/s11517-019-02111-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,22]]},"assertion":[{"value":"28 June 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}