{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T01:19:54Z","timestamp":1767057594863,"version":"3.48.0"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030668426"},{"type":"electronic","value":"9783030668433"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-66843-3_11","type":"book-chapter","created":{"date-parts":[[2020,12,30]],"date-time":"2020-12-30T11:03:20Z","timestamp":1609326200000},"page":"108-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Large-Scale Unbiased Neuroimage Indexing via 3D GPU-SIFT Filtering and\u00a0Keypoint Masking"],"prefix":"10.1007","author":[{"given":"\u00c9tienne","family":"Pepin","sequence":"first","affiliation":[]},{"given":"Jean-Baptiste","family":"Carluer","sequence":"additional","affiliation":[]},{"given":"Laurent","family":"Chauvin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7567-4283","authenticated-orcid":false,"given":"Matthew","family":"Toews","sequence":"additional","affiliation":[]},{"given":"Rola","family":"Harmouche","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,12,31]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Azad, R., Fayjie, A.R., Kauffman, C., Ayed, I.B., Pedersoli, M., Dolz, J.: On the texture bias for few-shot CNN segmentation (2020)","DOI":"10.1109\/WACV48630.2021.00272"},{"key":"11_CR2","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.cviu.2013.10.007","volume":"118","author":"M Bj\u00f6rkman","year":"2014","unstructured":"Bj\u00f6rkman, M., Bergstr\u00f6m, N., Kragic, D.: Detecting, segmenting and tracking unknown objects using multi-label MRF inference. Comput. Vis. Image Underst. 118, 111\u2013127 (2014)","journal-title":"Comput. Vis. Image Underst."},{"key":"11_CR3","unstructured":"Carluer, J.-B., Chauvin, L., Luo, J., Wells III, W.M., Machado, I., Toews, M.: GPU-based parallel optimisation of the 3D sift-rank algorithm and a novel brief-inspired 3d fast descriptor (2020, in preparation)"},{"key":"11_CR4","doi-asserted-by":"crossref","unstructured":"Chauvin, L., et al.: Neuroimage signature from salient keypoints is highly specific to individuals and shared by close relatives. NeuroImage (2019)","DOI":"10.1016\/j.neuroimage.2019.116208"},{"issue":"12","key":"11_CR5","doi-asserted-by":"publisher","first-page":"1566","DOI":"10.1016\/j.acra.2013.09.010","volume":"20","author":"J Doshi","year":"2013","unstructured":"Doshi, J., Erus, G., Yangming, O., Gaonkar, B., Davatzikos, C.: Multi-atlas skull-stripping. Acad. Radiol. 20(12), 1566\u20131576 (2013)","journal-title":"Acad. Radiol."},{"issue":"3","key":"11_CR6","doi-asserted-by":"publisher","first-page":"2362","DOI":"10.1016\/j.neuroimage.2011.09.012","volume":"59","author":"SF Eskildsen","year":"2012","unstructured":"Eskildsen, S.F., et al.: BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 59(3), 2362\u20132373 (2012)","journal-title":"NeuroImage"},{"issue":"2","key":"11_CR7","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","volume":"62","author":"B Fischl","year":"2012","unstructured":"Fischl, B.: FreeSurfer. Neuroimage 62(2), 774\u2013781 (2012)","journal-title":"Neuroimage"},{"key":"11_CR8","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W.: ImageNet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness. In: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, 6\u20139 May 2019. OpenReview.net (2019)"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), pp. 80\u201389. IEEE (2018)","DOI":"10.1109\/DSAA.2018.00018"},{"key":"11_CR10","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097\u20131105. Curran Associates Inc. (2012)"},{"issue":"7553","key":"11_CR11","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(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"1\u20132","key":"11_CR12","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1080\/757582976","volume":"21","author":"T Lindeberg","year":"1994","unstructured":"Lindeberg, T.: Scale-space theory: a basic tool for analyzing structures at different scales. J. Appl. Stat. 21(1\u20132), 225\u2013270 (1994)","journal-title":"J. Appl. Stat."},{"key":"11_CR13","unstructured":"Lindholm, S., Kronander, J.: Accounting for uncertainty in medical data: a CUDA implementation of normalized convolution. In: Proceedings of SIGRAD 2011. Evaluations of Graphics and Visualization-Efficiency; Usefulness; Accessibility; Usability, 17\u201318 November 2011, no. 065, pp. 35\u201342. KTH, Stockholm. Link\u00f6ping University Electronic Press (2011)"},{"issue":"2","key":"11_CR14","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","volume":"60","author":"DG Lowe","year":"2004","unstructured":"Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91\u2013110 (2004). https:\/\/doi.org\/10.1023\/B:VISI.0000029664.99615.94","journal-title":"Int. J. Comput. Vis."},{"issue":"10","key":"11_CR15","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (brats). IEEE Trans. Med. Imaging 34(10), 1993\u20132024 (2014)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"4","key":"11_CR16","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1145\/378456.378514","volume":"22","author":"DP Mitchell","year":"1988","unstructured":"Mitchell, D.P., Netravali, A.N.: Reconstruction filters in computer-graphics. SIGGRAPH Comput. Graph. 22(4), 221\u2013228 (1988)","journal-title":"SIGGRAPH Comput. Graph."},{"issue":"11","key":"11_CR17","doi-asserted-by":"publisher","first-page":"2227","DOI":"10.1109\/TPAMI.2014.2321376","volume":"36","author":"M Muja","year":"2014","unstructured":"Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227\u20132240 (2014)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR18","unstructured":"Ono, Y., Trulls, E., Fua, P., Yi, K.M.: LF-Net: learning local features from images. In: Advances in Neural Information Processing Systems, pp. 6234\u20136244 (2018)"},{"key":"11_CR19","unstructured":"Ritter, S., Barrett, D.G.T., Santoro, A., Botvinick, M.M.: Cognitive psychology for deep neural networks: a shape bias case study. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pp. 2940\u20132949, 06\u201311 August 2017. International Convention Centre, Sydney. PMLR (2017)"},{"key":"11_CR20","unstructured":"Sinha, S.N., Frahm, J.-M., Pollefeys, M., Genc, Y.: GPU-based video feature tracking and matching. In: EDGE, Workshop on Edge Computing Using New Commodity Architectures, vol. 278, p. 4321 (2006)"},{"issue":"3","key":"11_CR21","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1002\/hbm.10062","volume":"17","author":"SM Smith","year":"2002","unstructured":"Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143\u2013155 (2002)","journal-title":"Hum. Brain Mapp."},{"issue":"3","key":"11_CR22","doi-asserted-by":"publisher","first-page":"1060","DOI":"10.1016\/j.neuroimage.2004.03.032","volume":"22","author":"F S\u00e9gonne","year":"2004","unstructured":"S\u00e9gonne, F., et al.: A hybrid approach to the skull stripping problem in MRI. NeuroImage 22(3), 1060\u20131075 (2004)","journal-title":"NeuroImage"},{"issue":"3","key":"11_CR23","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.media.2012.11.002","volume":"17","author":"M Toews","year":"2013","unstructured":"Toews, M., Wells III, W.M.: Efficient and robust model-to-image alignment using 3D scale-invariant features. Med Image Anal. 17(3), 271\u201382 (2013)","journal-title":"Med Image Anal."},{"key":"11_CR24","doi-asserted-by":"crossref","unstructured":"Van Essen, D.C., Smith, S.M., Barch, D.M., Behrens, T.E.J., Yacoub, E., Ugurbil, K., Wu-Minn HCP Consortium, et al.: The Wu-Minn human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","DOI":"10.1016\/j.neuroimage.2013.05.041"},{"issue":"2","key":"11_CR25","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1109\/TMI.2018.2851194","volume":"39","author":"C Wachinger","year":"2020","unstructured":"Wachinger, C., Toews, M., Langs, G., Wells, W., Golland, P.: Keypoint transfer for fast whole-body segmentation. IEEE Trans. Med. Imaging 39(2), 273\u2013282 (2020)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"11_CR26","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1016\/j.neuroimage.2017.02.035","volume":"170","author":"C Wachinger","year":"2018","unstructured":"Wachinger, C., Reuter, M., Klein, T.: DeepNAT: deep convolutional neural network for segmenting neuroanatomy. NeuroImage 170, 434\u2013445 (2018)","journal-title":"NeuroImage"},{"key":"11_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/978-3-319-46466-4_28","volume-title":"Computer Vision \u2013 ECCV 2016","author":"KM Yi","year":"2016","unstructured":"Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: LIFT: learned invariant feature transform. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 467\u2013483. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46466-4_28"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-66843-3_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T01:05:32Z","timestamp":1767056732000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-66843-3_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030668426","9783030668433"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-66843-3_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"31 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnws.com\/","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":"28","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":"18","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":"64% - 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","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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The workshop was held virtually due to the COVID-19 pandemic.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}