{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:51:02Z","timestamp":1743025862039,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031728440"},{"type":"electronic","value":"9783031728457"}],"license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72845-7_16","type":"book-chapter","created":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T06:05:05Z","timestamp":1729145105000},"page":"221-232","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Classification of Lung Nodules on CT via Pseudo-colour Images and Deep Features from Pre-trained Convolutional Networks"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3371-1928","authenticated-orcid":false,"given":"Francesco","family":"Bianconi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3104-8782","authenticated-orcid":false,"given":"Mario Luca","family":"Fravolini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Elena","family":"Caltana","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Muhammad Usama","family":"Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2475-343X","authenticated-orcid":false,"given":"Barbara","family":"Palumbo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,18]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","first-page":"18962","DOI":"10.1109\/ACCESS.2021.3054735","volume":"9","author":"I Ali","year":"2021","unstructured":"Ali, I., Muzammil, M., Haq, I.U., Khaliq, A.A., Abdullah, S.: Deep feature selection and decision level fusion for lungs nodule classification. IEEE Access 9, 18962\u201318973 (2021)","journal-title":"IEEE Access"},{"key":"16_CR2","unstructured":"American Cancer Society: Key statistics for lung cancer, available on line at https:\/\/www.cancer.org\/cancer\/types\/lung-cancer\/about\/key-statistics.html. Accessed 8 Feb 2024"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Armato, S.G., III., et al.: LUNGx challenge for computerized lung nodule classification. J. Med. Imaging 3(4), 044506 (2016)","DOI":"10.1117\/1.JMI.3.4.044506"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Armato, S., III., et al.: The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38(2), 915\u2013931 (2011)","DOI":"10.1118\/1.3528204"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"737368","DOI":"10.3389\/fonc.2021.737368","volume":"11","author":"M Astaraki","year":"2021","unstructured":"Astaraki, M., Yang, G., Zakko, Y., Toma-Dasu, I., Smedby, O., Wang, C.: A comparative study of radiomics and deep-Learning based methods for pulmonary nodule malignancy prediction in low dose CT images. Front. Oncol. 11, 737368 (2021)","journal-title":"Front. Oncol."},{"issue":"13","key":"16_CR6","doi-asserted-by":"publisher","first-page":"5044","DOI":"10.3390\/s22135044","volume":"22","author":"F Bianconi","year":"2022","unstructured":"Bianconi, F., et al.: Form factors as potential imaging biomarkers to differentiate benign vs. malignant lung lesions on CT scans. Sensors 22(13), 5044 (2022)","journal-title":"Sensors"},{"issue":"10","key":"16_CR7","doi-asserted-by":"publisher","first-page":"1718","DOI":"10.3390\/app10051718","volume":"5","author":"F Bianconi","year":"2020","unstructured":"Bianconi, F., Palumbo, I., Spanu, A., Nuvoli, S., Fravolini, M.L., Palumbo, B.: PET\/CT radiomics in lung cancer: an overview. Appl. Sci. 5(10), 1718 (2020)","journal-title":"Appl. Sci."},{"issue":"1","key":"16_CR8","doi-asserted-by":"publisher","first-page":"2313311","DOI":"10.1080\/20018525.2024.2313311","volume":"11","author":"M Borg","year":"2024","unstructured":"Borg, M., Bodtger, U., Kristensen, K., Alstrup, G., Mamaeva, T., Arshad, A., Laursen, C.B., Hilberg, O., Andersen, M.B., Rasmussen, T.R.: Incidental pulmonary nodules may lead to a high proportion of early-stage lung cancer: but it requires more than a high CT volume to achieve this. Eur. Clin. Respir. J. 11(1), 2313311 (2024)","journal-title":"Eur. Clin. Respir. J."},{"issue":"6","key":"16_CR9","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., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., Prior, F.: The cancer imaging archive (TCIA): maintaining and operating a public information repository. J. Digit. Imaging 26(6), 1045\u20131057 (2013)","journal-title":"J. Digit. Imaging"},{"issue":"4","key":"16_CR10","doi-asserted-by":"publisher","first-page":"1083","DOI":"10.1016\/j.ijrobp.2017.12.268","volume":"102","author":"GJR Cook","year":"2018","unstructured":"Cook, G.J.R., Azad, G., Owczarczyk, K., Siddique, M., Goh, V.: Challenges and promises of PET radiomics. Int. J. Radiat. Oncol. Biol. Phys. 102(4), 1083\u20131089 (2018)","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"issue":"3","key":"16_CR11","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.3390\/s21031010","volume":"21","author":"C Cusano","year":"2021","unstructured":"Cusano, C., Napoletano, P., Schettini, R.: T1k+: a database for benchmarking color texture classification and retrieval methods. Sensors 21(3), 1010 (2021)","journal-title":"Sensors"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Da\u00a0N\u00f3brega, R.V.M., Peixoto, S.A., Da\u00a0Silva, S.P.P., Filho, P.P.R.: Lung nodule classification via deep transfer learning in CT lung images, pp. 244\u2013249. Karlstad, Sweden (2018)","DOI":"10.1109\/CBMS.2018.00050"},{"issue":"9","key":"16_CR13","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1016\/j.crad.2009.03.006","volume":"64","author":"A Edey","year":"2009","unstructured":"Edey, A., Hansell, D.: Incidentally detected small pulmonary nodules on CT. Clin. Radiol. 64(9), 872\u2013884 (2009)","journal-title":"Clin. Radiol."},{"issue":"1","key":"16_CR14","first-page":"287","volume":"59","author":"M Hatt","year":"2018","unstructured":"Hatt, M., Vallieres, M., Visvikis, D., Zwanenburg, A.: IBSI: an international community radiomics standardization initiative. J. Nuclear Med. 59(1), 287 (2018)","journal-title":"J. Nuclear Med."},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778. IEEE Computer Society (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"1","key":"16_CR16","doi-asserted-by":"publisher","first-page":"297","DOI":"10.18421\/TEM111-37","volume":"11","author":"J Hern\u00e1ndez-Rodr\u00edguez","year":"2022","unstructured":"Hern\u00e1ndez-Rodr\u00edguez, J., Cabrero-Fraile, F.J., Rodr\u00edguez-Conde, M.J.: Convolutional neural networks for multi-scale lung nodule classification in CT: influence of hyperparameter tuning on performance. TEM J. 11(1), 297\u2013306 (2022)","journal-title":"TEM J."},{"issue":"3","key":"16_CR17","doi-asserted-by":"publisher","first-page":"e106","DOI":"10.1016\/S2589-7500(19)30062-7","volume":"1","author":"A Hosny","year":"2019","unstructured":"Hosny, A., Aerts, H.J., Mak, R.H.: Handcrafted versus deep learning radiomics for prediction of cancer therapy response. Lancet Dig. Health 1(3), e106\u2013e107 (2019)","journal-title":"Lancet Dig. Health"},{"key":"16_CR18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, H., Lin, Y., Yao, Z., Xie, Z., Wei, Y., Ning, J., Cao, Y., Zhang, Z., Dong, L., Wei, F., Guo, B.: Swin transformer V2: scaling up capacity and resolution. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12009\u201312019. New Orleans, United States (2022)","DOI":"10.1109\/CVPR52688.2022.01170"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A ConvNet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11976\u201311986. New Orleans, United States (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"16_CR20","doi-asserted-by":"publisher","first-page":"113415","DOI":"10.1109\/ACCESS.2021.3102707","volume":"9","author":"M Muzammil","year":"2021","unstructured":"Muzammil, M., Ali, I., Haq, I.U., Khaliq, A.A., Abdullah, S.: Pulmonary nodule classification using feature and ensemble learning-based fusion techniques. IEEE Access 9, 113415\u2013113427 (2021)","journal-title":"IEEE Access"},{"key":"16_CR21","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","volume":"71","author":"L Nanni","year":"2017","unstructured":"Nanni, L., Ghidoni, S., Brahnam, S.: Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recogn. 71, 158\u2013172 (2017)","journal-title":"Pattern Recogn."},{"issue":"16","key":"16_CR22","doi-asserted-by":"publisher","first-page":"4786","DOI":"10.1158\/0008-5472.CAN-18-0125","volume":"78","author":"C Nioche","year":"2018","unstructured":"Nioche, C., Orlhac, F., Boughdad, S., Reuze, S., Goya-Outi, J., Robert, C., Pellot-Barakat, C., Soussan, M., Frouin, F.E., Buvat, I.: LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 78(16), 4786\u20134789 (2018)","journal-title":"Cancer Res."},{"key":"16_CR23","doi-asserted-by":"publisher","first-page":"696","DOI":"10.3390\/diagnostics10090696","volume":"10","author":"B Palumbo","year":"2020","unstructured":"Palumbo, B., Bianconi, F., Palumbo, I., Fravolini, M.L., Minestrini, M., Nuvoli, S., Stazza, M.L., Rondini, M., Spanu, A.: Value of shape and texture features from 18F-FDG PET\/CT to discriminate between benign and malignant solitary pulmonary nodules: an experimental evaluation. Diagnostics 10, 696 (2020)","journal-title":"Diagnostics"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Razavian, A., Azizpour, H., Sullivan, J., Carlsson, S.: CNN features off-the-shelf: An astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014. pp. 512\u2013519. Columbus, United States (2014)","DOI":"10.1109\/CVPRW.2014.131"},{"key":"16_CR25","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1186\/1471-2105-11-274","volume":"11","author":"B Schmid","year":"2010","unstructured":"Schmid, B., Schindelin, J., Cardona, A., Longair, M., Heisenberg, M.: A high-level 3D visualization API for Java and ImageJ. BMC Bioinform. 11, 274 (2010)","journal-title":"BMC Bioinform."},{"key":"16_CR26","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.792062","volume":"11","author":"K Sun","year":"2021","unstructured":"Sun, K., Chen, S., Zhao, J., Wang, B., Yang, Y., Wang, Y., Wu, C., Sun, X.: Convolutional neural network-based diagnostic model for a solid, indeterminate solitary pulmonary nodule or mass on computed tomography. Front. Oncol. 11, 792062 (2021)","journal-title":"Front. Oncol."},{"key":"16_CR27","unstructured":"Torch Contributors: Models and pre-trained weights (2024). Available online at http:\/\/pytorch.org\/vision\/stable\/models.html. Accessed 15 June 2024"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Van\u00a0Ginneken, B., Setio, A., Jacobs, C., Ciompi, F.: Off-the-shelf convolutional neural network features for pulmonary nodule detection in computed tomography scans. In: Proceedings of the 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015. pp. 286\u2013289. Brooklyn, United States (2015)","DOI":"10.1109\/ISBI.2015.7163869"},{"key":"16_CR29","doi-asserted-by":"publisher","first-page":"11322","DOI":"10.1038\/s41598-023-38350-z","volume":"13","author":"H Wang","year":"2023","unstructured":"Wang, H., Zhu, H., Ding, L., Yang, K.: A diagnostic classification of lung nodules using multiple-scale residual network. Sci. Rep. 13, 11322 (2023)","journal-title":"Sci. Rep."},{"issue":"5","key":"16_CR30","doi-asserted-by":"publisher","first-page":"051202","DOI":"10.1117\/1.JMI.7.5.051202","volume":"7","author":"J Yu","year":"2020","unstructured":"Yu, J., Yang, B., Wang, J., Leader, J., Wilson, D., Pu, J.: 2D CNN versus 3D CNN for false-positive reduction in lung cancer screening. J. Med. Imaging 7(5), 051202 (2020)","journal-title":"J. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Computational Color Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72845-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T06:07:29Z","timestamp":1729145249000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72845-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"ISBN":["9783031728440","9783031728457"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72845-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,10,18]]},"assertion":[{"value":"18 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The present study\u00a0was carried out on de-identified data from publicly available datasets, therefore does not qualify as research on human subjects.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"value":"The authors have\u00a0no competing interests to declare that are relevant to the content\u00a0of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"CCIW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Computational Color Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cciw2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ivl.disco.unimib.it\/cciw-2024","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}