https://scholars.lib.ntu.edu.tw/handle/123456789/553892
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Wang, Pochuan | en_US |
dc.contributor.author | Shen, Chen | en_US |
dc.contributor.author | Roth, Holger R. | en_US |
dc.contributor.author | Yang, Dong | en_US |
dc.contributor.author | Xu, Daguang | en_US |
dc.contributor.author | Oda, Masahiro | en_US |
dc.contributor.author | Misawa, Kazunari | en_US |
dc.contributor.author | PO-TING CHEN | en_US |
dc.contributor.author | KAO-LANG LIU | en_US |
dc.contributor.author | WEI-CHIH LIAO | en_US |
dc.contributor.author | WEICHUNG WANG | en_US |
dc.contributor.author | Kensaku, Mori | en_US |
dc.date.accessioned | 2021-03-19T05:35:19Z | - |
dc.date.available | 2021-03-19T05:35:19Z | - |
dc.date.issued | 2020-01-01 | - |
dc.identifier.isbn | 9783030605476 | - |
dc.identifier.issn | 03029743 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/553892 | - |
dc.description.abstract | © 2020, Springer Nature Switzerland AG. The performance of deep learning based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training. | en_US |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | en_US |
dc.subject | Federated learning | Neural architecture search | Pancreas segmentation | en_US |
dc.title | Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning | en_US |
dc.type | conference paper | en_US |
dc.identifier.doi | 10.1007/978-3-030-60548-3_19 | - |
dc.identifier.scopus | 2-s2.0-85092136143 | - |
dc.identifier.url | https://api.elsevier.com/content/abstract/scopus_id/85092136143 | - |
dc.relation.journalvolume | 12444 LNCS | en_US |
dc.relation.pageend | 200 | en_US |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
crisitem.author.dept | Radiology | - |
crisitem.author.dept | Medical Imaging-NTUH | - |
crisitem.author.dept | Radiology | - |
crisitem.author.dept | Medical Imaging-NTUH | - |
crisitem.author.dept | Medical Imaging-NTUCC | - |
crisitem.author.dept | Internal Medicine-NTUH | - |
crisitem.author.dept | Integrated Diagnostics and Therapeutics-NTUH | - |
crisitem.author.dept | Applied Mathematical Sciences | - |
crisitem.author.dept | Center for Artificial Intelligence and Advanced Robotics | - |
crisitem.author.orcid | 0000-0002-8675-5863 | - |
crisitem.author.orcid | 0000-0003-4100-8909 | - |
crisitem.author.orcid | 0000-0001-5362-6953 | - |
crisitem.author.orcid | 0000-0002-6154-7750 | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | College of Medicine | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | National Taiwan University Cancer Center (NTUCC) | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | National Taiwan University Hospital | - |
crisitem.author.parentorg | College of Science | - |
crisitem.author.parentorg | Others: University-Level Research Centers | - |
顯示於: | 醫學院附設醫院 (臺大醫院) |
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