Konferans bildirisi Açık Erişim
Cetindag, Sabri Can; Yergin, Mert; Alis, Deniz; Oksuz, Ilkay
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Yergin, Mert</subfield> <subfield code="u">Hevi AI, Istanbul, Turkey</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Alis, Deniz</subfield> </datafield> <datafield tag="700" ind1=" " ind2=" "> <subfield code="a">Oksuz, Ilkay</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="a">Creative Commons Attribution</subfield> <subfield code="u">http://www.opendefinition.org/licenses/cc-by</subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1007/978-3-031-09002-8_51</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">Meta-learning for Medical Image Segmentation Uncertainty Quantification</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="a">Cetindag, Sabri Can</subfield> <subfield code="u">Istanbul Tech Univ, Dept Comp Engn, Istanbul, Turkey</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="o">oai:aperta.ulakbim.gov.tr:258015</subfield> <subfield code="p">user-tubitak-destekli-proje-yayinlari</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="2">opendefinition.org</subfield> <subfield code="a">cc-by</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2022-01-01</subfield> </datafield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="u">https://aperta.ulakbim.gov.trrecord/258015/files/bib-55c79ac5-4b18-4e77-a3fc-cfa0b05e1738.txt</subfield> <subfield code="z">md5:df727affe1ca2423c8470732a7437c78</subfield> <subfield code="s">226</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <controlfield tag="005">20230729075451.0</controlfield> <controlfield tag="001">258015</controlfield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">conferencepaper</subfield> </datafield> <datafield tag="711" ind1=" " ind2=" "> <subfield code="a">BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, BRAINLES 2021, PT II</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a">Inter-rater and intra-rater variability is a major challenge in medical image segmentation. Inconsistencies of manual segmentations between different experts can challenge development of deterministic automated medical image analysis tools. QUBIQ 2021 is a challenge to enable the successful development of automated machine learning tools, when there are inconsistencies between the labels of different annotators. In this paper, we propose to use meta-learning for quantifying uncertainty in biomedical image quantification. We first train a segmentation network for each expert separately with extensive data augmentation using the nnUnet framework. Then, a meta learner model based on a conventional U-net architecture is trained using the average of all annotators as ground truth, and output of all models that have been trained for each radiologist as input. We compared our results of meta-learning with ensemble methods for various image segmentation tasks and illustrate improved performance.</subfield> </datafield> </record>
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