{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:08:29Z","timestamp":1777043309256,"version":"3.51.4"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T00:00:00Z","timestamp":1621296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2021,6]]},"DOI":"10.1007\/s11517-021-02378-y","type":"journal-article","created":{"date-parts":[[2021,5,18]],"date-time":"2021-05-18T07:02:32Z","timestamp":1621321352000},"page":"1299-1310","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Automatic classification of solitary pulmonary nodules in PET\/CT imaging employing transfer learning techniques"],"prefix":"10.1007","volume":"59","author":[{"given":"Ioannis D.","family":"Apostolopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emmanuel G.","family":"Pintelas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ioannis E.","family":"Livieris","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dimitris J.","family":"Apostolopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolaos D.","family":"Papathanasiou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Panagiotis E.","family":"Pintelas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George S.","family":"Panayiotakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,18]]},"reference":[{"key":"2378_CR1","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.bspc.2017.11.017","volume":"41","author":"Zia ur Rehman M, Javaid M, Shah SIA","year":"2018","unstructured":"Zia ur Rehman M, Javaid M, Shah SIA, Gilani SO, Jamil M, Butt SI, (2018) An appraisal of nodules detection techniques for lung cancer in CT images. Biomed Signal Process Control 41:140\u2013151","journal-title":"Biomed Signal Process Control"},{"key":"2378_CR2","doi-asserted-by":"publisher","first-page":"1668","DOI":"10.1148\/rg.2015150023","volume":"35","author":"MA Bruno","year":"2015","unstructured":"Bruno MA, Walker EA, Abujudeh HH (2015) Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35:1668\u20131676","journal-title":"Radiographics"},{"key":"2378_CR3","doi-asserted-by":"publisher","first-page":"194","DOI":"10.3390\/s19010194","volume":"19","author":"X Wang","year":"2019","unstructured":"Wang X, Mao K, Wang L, Yang P, Lu D, He P (2019) An appraisal of lung nodules automatic classification algorithms for CT images. Sensors 19:194","journal-title":"Sensors"},{"key":"2378_CR4","doi-asserted-by":"crossref","unstructured":"Wu B, Zhou Z, Wang J, Wang Y (2018) Joint learning for pulmonary nodule segmentation, attributes and malignancy prediction. arXiv preprint arXiv.1802.03584","DOI":"10.1109\/ISBI.2018.8363765"},{"key":"2378_CR5","doi-asserted-by":"crossref","unstructured":"LeCun Y, Kavukcuoglu K, Farabet C (2010) Convolutional networks and applications in vision. In proceedings of 2010 IEEE International Symposium on Circuits and Systems, Paris, France. p. 253\u20136","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"2378_CR6","doi-asserted-by":"crossref","unstructured":"Zhu W, Liu C, Fan W, Xie X (2018) DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification. arXiv preprint arXiv.1801.09555","DOI":"10.1101\/189928"},{"key":"2378_CR7","doi-asserted-by":"crossref","unstructured":"Russakovsky O, Deng J, et al (2015) ImageNet large scale visual recognition challenge. arXiv preprint arXiv.14090.575","DOI":"10.1007\/s11263-015-0816-y"},{"key":"2378_CR8","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv.1409.1556"},{"key":"2378_CR9","unstructured":"Howard AG, Zhu M, et al (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv.1704.04861"},{"key":"2378_CR10","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/s10916-019-1327-0","volume":"43","author":"G Zhang","year":"2019","unstructured":"Zhang G, Yang Z et al (2019) An appraisal of nodule diagnosis for lung cancer in CT images. J Med Syst 43:181","journal-title":"J Med Syst"},{"key":"2378_CR11","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato","year":"2011","unstructured":"Armato SG, McLennan G et al (2011) The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans: the LIDC\/IDRI thoracic CT database of lung nodules. Med Phys 38:915\u2013931","journal-title":"Med Phys"},{"issue":"7","key":"2378_CR12","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1007\/s11517-019-01976-1","volume":"57","author":"G Chen","year":"2019","unstructured":"Chen G, Zhang J et al (2019) Identification of pulmonary nodules via CT images with hierarchical fully convolutional networks. Med Biol Eng Compu 57(7):1567\u20131580","journal-title":"Med Biol Eng Compu"},{"issue":"1","key":"2378_CR13","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 et al (2019) A novel fused convolutional neural network for biomedical image classification. Med Biol Eng Compu 57(1):107\u2013121","journal-title":"Med Biol Eng Compu"},{"key":"2378_CR14","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"H-C Shin","year":"2016","unstructured":"Shin H-C, Roth HR et al (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35:1285\u20131298","journal-title":"IEEE Trans Med Imaging"},{"key":"2378_CR15","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1007\/s11548-017-1696-0","volume":"13","author":"X Zhao","year":"2018","unstructured":"Zhao X, Liu L et al (2018) Agile convolutional neural network for pulmonary nodule classification using CT images. Int J CARS 13:585\u2013595","journal-title":"Int J CARS"},{"key":"2378_CR16","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2378_CR17","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84\u201390","journal-title":"Commun ACM"},{"key":"2378_CR18","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.patcog.2016.05.029","volume":"61","author":"W Shen","year":"2017","unstructured":"Shen W, Zhou M et al (2017) Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification. Pattern Recogn 61:663\u2013673","journal-title":"Pattern Recogn"},{"key":"2378_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/8314740","volume":"2017","author":"Q Song","year":"2017","unstructured":"Song Q, Zhao L et al (2017) Using deep learning for classification of lung nodules on computed tomography images. J Healthc Eng 2017:1\u20137","journal-title":"J Healthc Eng"},{"key":"2378_CR20","doi-asserted-by":"publisher","first-page":"9286","DOI":"10.1038\/s41598-018-27569-w","volume":"8","author":"JL Causey","year":"2018","unstructured":"Causey JL, Zhang J et al (2018) Highly accurate model for prediction of lung nodule malignancy with CT scans. Sci Rep 8:9286","journal-title":"Sci Rep"},{"key":"2378_CR21","doi-asserted-by":"crossref","unstructured":"Dey R, Lu Z, Hong Y (2018) Diagnostic classification of lung nodules using 3D neural networks. arXiv preprint arXiv.1803.07192","DOI":"10.1109\/ISBI.2018.8363687"},{"key":"2378_CR22","unstructured":"Huang G, Liu Z, et al (2018) Densely Connected Convolutional Networks. arXiv preprint arXiv.1608.06993"},{"key":"2378_CR23","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1109\/CBMS.2018.00050","volume-title":"IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS)","author":"RV Nobrega","year":"2018","unstructured":"Nobrega RV, Peixoto SA et al (2018) Lung Nodule Classification via Deep Transfer Learning in CT Lung Images. IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS). Karlstad, Sweden, pp 244\u2013249"},{"key":"2378_CR24","doi-asserted-by":"publisher","first-page":"615","DOI":"10.3233\/XST-180490","volume":"27","author":"X Zhao","year":"2019","unstructured":"Zhao X, Qi S et al (2019) Deep CNN models for pulmonary nodule classification: model modification, model integration, and transfer learning. XST 27:615\u2013629","journal-title":"XST"},{"key":"#cr-split#-2378_CR25.1","doi-asserted-by":"crossref","unstructured":"Xie Y, Xia Y, et al (2017) Transferable Multi-model Ensemble for Benign-Malignant Lung Nodule Classification on Chest CT. In Descoteaux M, Maier-Hein L, Franz A, Jannin P, Collins DL, Duchesne S","DOI":"10.1007\/978-3-319-66179-7_75"},{"key":"#cr-split#-2378_CR25.2","unstructured":"(ed) Medical Image Computing and Computer Assisted Intervention \u2212 MICCAI 2017, vol. 10435, Springer International Publishing, p. 656-64"},{"issue":"1","key":"2378_CR26","doi-asserted-by":"publisher","first-page":"17","DOI":"10.3390\/a13010017","volume":"13","author":"E Pintelas","year":"2020","unstructured":"Pintelas E, Livieris IE, Pintelas P (2020) A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability. Algorithms 13(1):17","journal-title":"Algorithms"},{"key":"2378_CR27","doi-asserted-by":"crossref","unstructured":"Pintelas E, Liaskos M, Livieris IE, Kotsiantis S, Pintelas P (2020) Explainable machine learning framework for image classification\u00a0problems: case study on Glioma cancer prediction. J Imaging, 6(6), 37, ID: jimaging-805312, https:\/\/www.mdpi.com\/journal\/jimaging\/special_issues\/dlmia","DOI":"10.3390\/jimaging6060037"},{"key":"2378_CR28","doi-asserted-by":"crossref","unstructured":"Oquab M, Bottou L, Laptev I, Sivic J (2014) Learning and transferring mid-level image representations using convolutional neural networks. In proceedings of the 2014 IEEE conference on computer vision and pattern recognition, Columbus, OH, USA, p. 1717\u20131724","DOI":"10.1109\/CVPR.2014.222"},{"key":"2378_CR29","doi-asserted-by":"crossref","unstructured":"Kwasigroch A, Mikolajczyk A, Grochowski M (2017) Deep neural networks approach to skin lesions classification \u2014 a comparative analysis. In proceedings of the 22nd International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, p. 1069\u201374","DOI":"10.1109\/MMAR.2017.8046978"},{"key":"#cr-split#-2378_CR30.1","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab N, Hornegger J, Wells WM, Frangi AF,","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"#cr-split#-2378_CR30.2","unstructured":"(ed) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, vol. 9351, Springer International Publishing, p. 234-41"},{"key":"2378_CR31","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.1007\/s11548-017-1605-6","volume":"12","author":"A Nibali","year":"2017","unstructured":"Nibali A, He Z, Wollersheim D (2017) Pulmonary nodule classification with deep residual networks. Int J CARS 12:1799\u20131808","journal-title":"Int J CARS"},{"key":"2378_CR32","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1007\/s10278-013-9622-7","volume":"26","author":"K Clark","year":"2013","unstructured":"Clark K, Vendt B et al (2013) (2013) The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 26:1045\u20131057","journal-title":"J Digit Imaging"},{"key":"2378_CR33","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep Learning with Depthwise Separable Convolutions. arXiv preprint arXiv.1610.02357","DOI":"10.1109\/CVPR.2017.195"},{"key":"2378_CR34","first-page":"244","volume":"6","author":"JZ Cheng","year":"2016","unstructured":"Cheng JZ, Ni D et al (2016) Computer-Aided Diagnosis with Deep Learning Architecture: Applications to Breast Lesions in US Images and Pulmonary Nodules in CT Scans. Sci Rep 6:244\u2013254","journal-title":"Sci Rep"},{"key":"2378_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.bspc.2017.08.026","volume":"40","author":"G Wei","year":"2018","unstructured":"Wei G, Ma H et al (2018) Lung nodule classification using local kernel regression models with out-of-sample extension. Biomed Signal Process Control 40:1\u20139","journal-title":"Biomed Signal Process Control"},{"issue":"12","key":"2378_CR36","doi-asserted-by":"publisher","first-page":"2757","DOI":"10.1007\/s11517-019-02060-4","volume":"57","author":"ME Bayrakdar","year":"2019","unstructured":"Bayrakdar ME (2019) Priority based health data monitoring with IEEE 802 11af technology in wireless medical sensor networks. Med Biol Eng Comput 57(12):2757\u20132769","journal-title":"Med Biol Eng Comput"},{"key":"2378_CR37","doi-asserted-by":"publisher","first-page":"1517","DOI":"10.1016\/j.egypro.2018.11.317","volume":"157","author":"CM Michail","year":"2019","unstructured":"Michail CM, Agavanakis KN, Karpetas GE et al (2019) Information content in nuclear medicine imaging. Energy Procedia 157:1517\u20131524. https:\/\/doi.org\/10.1016\/j.egypro.2018.11.317","journal-title":"Energy Procedia"},{"key":"2378_CR38","doi-asserted-by":"crossref","unstructured":"Bayrakdar ME (2019) Fuzzy logic based coordinator node selection approach in wireless medical sensor networks. In 2019 4th International Conference on Computer Science and Engineering (UBMK) (pp. 340\u2013343). Presented at the 2019 4th International Conference on Computer Science and Engineering (UBMK), Samsun, Turkey: IEEE.","DOI":"10.1109\/UBMK.2019.8907097"},{"issue":"1","key":"2378_CR39","doi-asserted-by":"publisher","first-page":"9","DOI":"10.21928\/uhdjst.v4n1y2020.pp9-17","volume":"4","author":"Z Mohammed","year":"2020","unstructured":"Mohammed Z, Abdulla A (2020) Thresholding-based white blood cells segmentation from microscopic blood images. UHD J Sci Technol 4(1):9","journal-title":"UHD J Sci Technol"},{"key":"2378_CR40","unstructured":"Goodfellow I, Pouget-Abadie J, et al (2014) Generative adversarial nets. In Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ, (ed) Advances in neural information processing systems 27, Curran Associates, p. 2672\u20132680"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02378-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-021-02378-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-021-02378-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T01:10:05Z","timestamp":1623719405000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-021-02378-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,18]]},"references-count":42,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["2378"],"URL":"https:\/\/doi.org\/10.1007\/s11517-021-02378-y","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,18]]},"assertion":[{"value":"21 March 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}