{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:34:23Z","timestamp":1742913263136,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031250811"},{"type":"electronic","value":"9783031250828"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-25082-8_30","type":"book-chapter","created":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T09:12:42Z","timestamp":1676106762000},"page":"457-471","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable Model for\u00a0Localization of\u00a0Spiculation in\u00a0Lung Nodules"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4252-7746","authenticated-orcid":false,"given":"Mirtha","family":"Lucas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4703-6417","authenticated-orcid":false,"given":"Miguel","family":"Lerma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1015-6290","authenticated-orcid":false,"given":"Jacob","family":"Furst","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2165-7234","authenticated-orcid":false,"given":"Daniela","family":"Raicu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,2,12]]},"reference":[{"key":"30_CR1","doi-asserted-by":"publisher","unstructured":"Andrejeva, L., Geisel, J.L., Harigopa, M.: Spiculated masses, breast imaging. Oxford Medicine Online (2018). https:\/\/doi.org\/10.1093\/med\/9780190270261.003.0025","DOI":"10.1093\/med\/9780190270261.003.0025"},{"key":"30_CR2","unstructured":"Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning Workshop, vol. 27, pp. 17\u201337 (2011)"},{"key":"30_CR3","doi-asserted-by":"publisher","unstructured":"Chattopadhyay, A., Sarkar, A., Howlader, P., Balasubramanian, V.N.: Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) (2018). https:\/\/doi.org\/10.1109\/wacv.2018.00097","DOI":"10.1109\/wacv.2018.00097"},{"key":"30_CR4","series-title":"Springer Texts in Statistics","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1007\/978-1-4614-0391-3_3","volume-title":"Modern Mathematical Statistics with Applications","author":"JL Devore","year":"2012","unstructured":"Devore, J.L., Berk, K.N.: Discrete random variables and probability distributions. In: Devore, J.L., Berk, K.N. (eds.) Modern Mathematical Statistics with Applications. STS, pp. 96\u2013157. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4614-0391-3_3"},{"issue":"4","key":"30_CR5","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.3.4.044504","volume":"3","author":"MC Hancock","year":"2016","unstructured":"Hancock, M.C., Magnan, J.F.: Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the lung image database consortium dataset with two statistical learning methods. J. Med. Imaging 3(4), 044504 (2016)","journal-title":"J. Med. Imaging"},{"issue":"02","key":"30_CR6","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1142\/S0218488598000094","volume":"06","author":"S Hochreite","year":"1998","unstructured":"Hochreite, S.: The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 06(02), 107\u2013116 (1998)","journal-title":"Int. J. Uncertain. Fuzziness Knowl.-Based Syst."},{"key":"30_CR7","unstructured":"Hofer, M.: CT Teaching Manual, A Systematic Approach to CT Reading. Thieme Publishing Group (2007)"},{"issue":"28","key":"30_CR8","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.canlet.2019.12.007","volume":"471","author":"S Huanga","year":"2020","unstructured":"Huanga, S., Yang, J., Fong, S., Zhao, Q.: Artificial intelligence in cancer diagnosis and prognosis: opportunities and challenges. Cancer Lett. 471(28), 61\u201371 (2020)","journal-title":"Cancer Lett."},{"key":"30_CR9","doi-asserted-by":"publisher","unstructured":"Armato III, S.G., et al.: Lung image database consortium: developing a resource for the medical imaging research community. Radiology 232(3), 739\u2013748 (2004). https:\/\/doi.org\/10.1148\/radiol.2323032035","DOI":"10.1148\/radiol.2323032035"},{"issue":"4","key":"30_CR10","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1287\/mnsc.6.4.366","volume":"6","author":"LV Kantorovich","year":"1939","unstructured":"Kantorovich, L.V.: Mathematical methods of organizing and planning production. Manage. Sci. 6(4), 366\u2013422 (1939)","journal-title":"Manage. Sci."},{"key":"30_CR11","doi-asserted-by":"publisher","unstructured":"Lao, Z., Zheng, X.: Multiscale quantification of tissue spiculation and distortion for detection of architectural distortion and spiculated mass in mammography. In: M.D., R.M.S., van Ginneken, B. (eds.) Medical Imaging 2011: Computer-Aided Diagnosis, vol. 7963, pp. 468\u2013475. International Society for Optics and Photonics, SPIE (2011). https:\/\/doi.org\/10.1117\/12.877330","DOI":"10.1117\/12.877330"},{"key":"30_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436\u2013444 (2015)","journal-title":"Nature"},{"key":"30_CR13","doi-asserted-by":"crossref","unstructured":"Lucas, M., Lerma, M., Furst, J., Raicu, D.: Visual explanations from deep networks via Riemann-Stieltjes integrated gradient-based localization (2022). https:\/\/arxiv.org\/abs\/2205.10900","DOI":"10.1007\/978-3-031-20713-6_20"},{"issue":"1","key":"30_CR14","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1214\/aoms\/1177730491","volume":"18","author":"H Mann","year":"1947","unstructured":"Mann, H., Whitney, D.: On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18(1), 50\u201360 (1947)","journal-title":"Ann. Math. Stat."},{"issue":"1","key":"30_CR15","doi-asserted-by":"publisher","first-page":"6","DOI":"10.33137\/juls.v14i1.35833","volume":"14","author":"DMM Mendez","year":"2020","unstructured":"Mendez, D.M.M., Berm\u00fadez, A., Tyrrell, P.N.: Visualization of layers within a convolutional neural network using gradient activation maps. J. Undergraduate Life Sci. 14(1), 6 (2020)","journal-title":"J. Undergraduate Life Sci."},{"key":"30_CR16","doi-asserted-by":"publisher","unstructured":"Protter, M.H., Morrey, C.B.: The Riemann-Stieltjes integral and functions of bounded variation. In: Protter, M.H., Morrey, C.B. (eds.) A First Course in Real Analysis. Undergraduate Texts in Mathematics. Springer, New York (1991). https:\/\/doi.org\/10.1007\/978-1-4419-8744-0_12","DOI":"10.1007\/978-1-4419-8744-0_12"},{"key":"30_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105839","author":"WCS Nadeem","year":"2020","unstructured":"Nadeem, W.C.S., Alam, S.R., Deasy, J.O., Tannenbaum, A., Lu, W.: Reproducible and interpretable spiculation quantification for lung cancer screening. Comput. Methods Programs Biomed. (2020). https:\/\/doi.org\/10.1016\/j.cmpb.2020.105839","journal-title":"Comput. Methods Programs Biomed."},{"key":"30_CR18","doi-asserted-by":"crossref","unstructured":"Paci, E., et al.: Ma01.09 mortality, survival and incidence rates in the italung randomised lung cancer screening trial (Italy). J. Thorac. Oncol. 12(1), S346\u2013S347 (2017)","DOI":"10.1016\/j.jtho.2016.11.379"},{"key":"30_CR19","doi-asserted-by":"publisher","unstructured":"Qiu, B., Furst, J., Rasin, A., Tchoua, R., Raicu, D.: Learning latent spiculated features for lung nodule characterization. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1254\u20131257 (2020). https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175720","DOI":"10.1109\/EMBC44109.2020.9175720"},{"key":"30_CR20","doi-asserted-by":"publisher","unstructured":"Qiu, S., Sun, J., Zhou, T., Gao, G., He, Z., Liang, T.: Spiculation sign recognition in a pulmonary nodule based on spiking neural p systems. BioMed Res. Int. 2020 (2020). https:\/\/doi.org\/10.1155\/2020\/6619076","DOI":"10.1155\/2020\/6619076"},{"issue":"2","key":"30_CR21","doi-asserted-by":"publisher","first-page":"47","DOI":"10.3390\/e19020047","volume":"19","author":"A Ramdas","year":"2017","unstructured":"Ramdas, A., Garcia, N., Cuturi, M.: On Wasserstein two sample testing and related families of nonparametric tests. Entropy 19(2), 47 (2017). https:\/\/doi.org\/10.3390\/e19020047","journal-title":"Entropy"},{"issue":"1","key":"30_CR22","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1002\/wics.1375","volume":"8","author":"ML Rizzo","year":"2015","unstructured":"Rizzo, M.L., Sz\u00e9kely, G.J.: Energy distance. Wiley Interdiscip. Rev. Comput. Stat. 8(1), 27\u201338 (2015)","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"issue":"3","key":"30_CR23","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211\u2013252 (2015). https:\/\/doi.org\/10.1007\/s11263-015-0816-y","journal-title":"Int. J. Comput. Vision"},{"key":"30_CR24","doi-asserted-by":"crossref","unstructured":"Sattarzadeh, S., Sudhakar, M., Plataniotis, K.N., Jang, J., Jeong, Y., Kim, H.: Integrated Grad-CAM: sensitivity-aware visual explanation of deep convolutional networks via integrated gradient-based scoring (2021). https:\/\/arxiv.org\/abs\/2102.07805","DOI":"10.1109\/ICASSP39728.2021.9415064"},{"issue":"2","key":"30_CR25","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2019","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128(2), 336\u2013359 (2019). https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int. J. Comput. Vision"},{"key":"30_CR26","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2015). https:\/\/arxiv.org\/abs\/1409.1556"},{"key":"30_CR27","unstructured":"Sundararajan, M., Taly, A., Yan, Q.: Axiomatic attribution for deep networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 3319\u20133328. PMLR (2017). https:\/\/proceedings.mlr.press\/v70\/sundararajan17a.html"},{"key":"30_CR28","unstructured":"Szekely, G.J.: E-statistics: the energy of statistical samples. Technical report, Bowling Green State University, Department of Mathematics and Statistics (2002)"},{"key":"30_CR29","unstructured":"Waserstein, L.N.: Markov processes over denumerable products of spaces, describing large systems of automata. Problemy Peredac\u0306i Informacii 5(3), 6\u201372 (1969)"},{"key":"30_CR30","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.media.2019.03.010","volume":"55","author":"M Winkels","year":"2019","unstructured":"Winkels, M., Cohena, T.S.: Pulmonary nodule detection in CT scans with equivariant CNNs. Med. Image Anal. 55, 15\u201326 (2019)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25082-8_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T13:07:30Z","timestamp":1709816850000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25082-8_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031250811","9783031250828"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25082-8_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"12 February 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1645","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"28% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.21","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.91","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"From the workshops, 367 reviewed full papers have been selected for publication","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}