乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T13:01:52Z","timestamp":1726146112926},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030908737"},{"type":"electronic","value":"9783030908744"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-90874-4_6","type":"book-chapter","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:03:05Z","timestamp":1636761785000},"page":"56-67","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development of the Next Generation Hand-Held Doppler with Waveform Phasicity Predictive Capabilities Using Deep Learning"],"prefix":"10.1007","author":[{"given":"Adrit","family":"Rao","sequence":"first","affiliation":[]},{"given":"Akshay","family":"Chaudhari","sequence":"additional","affiliation":[]},{"given":"Oliver","family":"Aalami","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,11,14]]},"reference":[{"key":"6_CR1","unstructured":"Nvidia tesla p100: The most advanced data center accelerator. https:\/\/www.nvidia.com\/en-us\/data-center\/tesla-p100\/"},{"key":"6_CR2","unstructured":"Peripheral arterial disease (pad) (September 2020). https:\/\/www.cdc.gov\/heartdisease\/PAD.htm"},{"key":"6_CR3","unstructured":"Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: 12th $$\\{$$USENIX$$\\}$$ Symposium on Operating systems Design and Implementation ($$\\{$$OSDI$$\\}$$ 16), pp. 265\u2013283 (2016)"},{"issue":"24","key":"6_CR4","doi-asserted-by":"publisher","first-page":"2890","DOI":"10.1161\/CIR.0b013e318276fbcb","volume":"126","author":"V Aboyans","year":"2012","unstructured":"Aboyans, V., et al.: Measurement and interpretation of the ankle-brachial index: a scientific statement from the American heart association. Circulation 126(24), 2890\u20132909 (2012)","journal-title":"Circulation"},{"key":"6_CR5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/978-1-4842-4470-8_7","volume-title":"Building Machine Learning and Deep Learning Models on Google Cloud Platform","author":"E Bisong","year":"2019","unstructured":"Bisong, E.: Google colaboratory. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform, pp. 59\u201364. Apress, Berkeley, CA (2019). https:\/\/doi.org\/10.1007\/978-1-4842-4470-8_7"},{"issue":"6","key":"6_CR6","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1111\/j.1532-5415.2008.01719.x","volume":"56","author":"JS Brach","year":"2008","unstructured":"Brach, J.S., et al.: Incident physical disability in people with lower extremity peripheral arterial disease: the role of cardiovascular disease. J. Am. Geriatr. Soc. 56(6), 1037\u20131044 (2008)","journal-title":"J. Am. Geriatr. Soc."},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Criqui, M.H.: Peripheral arterial disease-epidemiological aspects. Vasc. Med. 6(1_suppl), 3\u20137 (2001)","DOI":"10.1177\/1358836X0100600i102"},{"issue":"6","key":"6_CR8","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1056\/NEJM199202063260605","volume":"326","author":"MH Criqui","year":"1992","unstructured":"Criqui, M.H., et al.: Mortality over a period of 10 years in patients with peripheral arterial disease. N. Engl. J. Med. 326(6), 381\u2013386 (1992)","journal-title":"N. Engl. J. Med."},{"issue":"11","key":"6_CR9","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.286.11.1317","volume":"286","author":"AT Hirsch","year":"2001","unstructured":"Hirsch, A.T., et al.: Peripheral arterial disease detection, awareness, and treatment in primary care. JAMA 286(11), 1317\u20131324 (2001)","journal-title":"JAMA"},{"issue":"03","key":"6_CR10","first-page":"90","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter, J.D.: Matplotlib: a 2d graphics environment. IEEE Ann. Hist. Comput. 9(03), 90\u201395 (2007)","journal-title":"IEEE Ann. Hist. Comput."},{"key":"6_CR11","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097\u20131105 (2012)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"7553","key":"6_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(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"4","key":"6_CR13","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","volume":"18","author":"JG Lee","year":"2017","unstructured":"Lee, J.G., et al.: Deep learning in medical imaging: general overview. Korean J. Radiol. 18(4), 570 (2017)","journal-title":"Korean J. Radiol."},{"key":"6_CR14","doi-asserted-by":"crossref","unstructured":"McFee, B., et al.: Librosa: audio and music signal analysis in python. In: Proceedings of the 14th Python in Science Conference, vol. 8, pp. 18\u201325. Citeseer (2015)","DOI":"10.25080\/Majora-7b98e3ed-003"},{"issue":"9289","key":"6_CR15","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1016\/S0140-6736(01)06351-6","volume":"358","author":"K Ouriel","year":"2001","unstructured":"Ouriel, K.: Peripheral arterial disease. Lancet 358(9289), 1257\u20131264 (2001)","journal-title":"Lancet"},{"key":"6_CR16","unstructured":"Pi, R.: Raspberry pi 3 model b (2015) . https:\/\/www.raspberrypi.org"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Rao, A.: Waveform phasicity prediction from arterial sounds through spectrogram analysis using convolutional neural networks for limb perfusion assessment. arXiv preprint arXiv:2104.09748 (2021)","DOI":"10.1109\/MWSCAS47672.2021.9531856"},{"issue":"5","key":"6_CR18","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1177\/8756479308323128","volume":"24","author":"R Scissons","year":"2008","unstructured":"Scissons, R.: Characterizing triphasic, biphasic, and monophasic doppler waveforms: should a simple task be so difficult? J. Diagn. Med. Sonogr. 24(5), 269\u2013276 (2008)","journal-title":"J. Diagn. Med. Sonogr."},{"issue":"6","key":"6_CR19","doi-asserted-by":"publisher","first-page":"1688","DOI":"10.1109\/78.678493","volume":"46","author":"IW Selesnick","year":"1998","unstructured":"Selesnick, I.W., Burrus, C.S.: Generalized digital butterworth filter design. IEEE Trans. Signal Process. 46(6), 1688\u20131694 (1998)","journal-title":"IEEE Trans. Signal Process."},{"key":"6_CR20","doi-asserted-by":"crossref","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. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"6_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"issue":"1","key":"6_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13047-014-0058-1","volume":"8","author":"PE Tehan","year":"2015","unstructured":"Tehan, P.E., Chuter, V.H.: Use of hand-held doppler ultrasound examination by podiatrists: a reliability study. J. Foot Ankle Res. 8(1), 1\u20137 (2015)","journal-title":"J. Foot Ankle Res."},{"key":"6_CR23","doi-asserted-by":"crossref","unstructured":"Torrey, L., Shavlik, J.: Transfer learning. In: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp. 242\u2013264. IGI global (2010)","DOI":"10.4018\/978-1-60566-766-9.ch011"},{"key":"6_CR24","unstructured":"Virtanen, P., et al.: Scipy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17(3), 261\u2013272 (2020)"}],"container-title":["Lecture Notes in Computer Science","Clinical Image-Based Procedures, Distributed and Collaborative Learning, Artificial Intelligence for Combating COVID-19 and Secure and Privacy-Preserving Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90874-4_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T00:03:26Z","timestamp":1636761806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90874-4_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030908737","9783030908744"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90874-4_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"14 November 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CLIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Clinical Image-Based Procedures","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"clip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-clip.org\/","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":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13","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":"9","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":"69% - 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":"2","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)"}}]}}