Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/71252

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Campo DCValorIdioma
dc.contributor.authorPereira, Sérgiopor
dc.contributor.authorMeier, Raphaelpor
dc.contributor.authorAlves, Victorpor
dc.contributor.authorReyes, Mauriciopor
dc.contributor.authorSilva, Carlos A.por
dc.date.accessioned2021-04-03T14:58:54Z-
dc.date.issued2018-
dc.identifier.citationPereira S., Meier R., Alves V., Reyes M., Silva C.A. (2018) Automatic Brain Tumor Grading from MRI Data Using Convolutional Neural Networks and Quality Assessment. In: Stoyanov D. et al. (eds) Understanding and Interpreting Machine Learning in Medical Image Computing Applications. MLCN 2018, DLF 2018, IMIMIC 2018. Lecture Notes in Computer Science, vol 11038. Springer, Cham. https://doi.org/10.1007/978-3-030-02628-8_12por
dc.identifier.isbn978-3-030-02627-1-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://hdl.handle.net/1822/71252-
dc.description.abstractGlioblastoma Multiforme is a high grade, very aggressive, brain tumor, with patients having a poor prognosis. Lower grade gliomas are less aggressive, but they can evolve into higher grade tumors over time. Patient management and treatment can vary considerably with tumor grade, ranging from tumor resection followed by a combined radio- and chemotherapy to a “wait and see” approach. Hence, tumor grading is important for adequate treatment planning and monitoring. The gold standard for tumor grading relies on histopathological diagnosis of biopsy specimens. However, this procedure is invasive, time consuming, and prone to sampling error. Given these disadvantages, automatic tumor grading from widely used MRI protocols would be clinically important, as a way to expedite treatment planning and assessment of tumor evolution. In this paper, we propose to use Convolutional Neural Networks for predicting tumor grade directly from imaging data. In this way, we overcome the need for expert annotations of regions of interest. We evaluate two prediction approaches: from the whole brain, and from an automatically defined tumor region. Finally, we employ interpretability methodologies as a quality assurance stage to check if the method is using image regions indicative of tumor grade for classification.por
dc.description.sponsorshipSérgio Pereira was supported by a scholarship from the Fundação para a Ciência e Tecnologia (FCT), Portugal (scholarship number PD/BD/105803/2014). This work is supported by FCT with the reference project UID/EEA/04436/2013, COMPETE 2020 with the code POCI-01-0145-FEDER-006941.por
dc.language.isoengpor
dc.publisherSpringer, Champor
dc.relationPD/BD/105803/2014por
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147325/PTpor
dc.rightsrestrictedAccesspor
dc.titleAutomatic brain tumor grading from MRI data using convolutional neural networks and quality assessmentpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007%2F978-3-030-02628-8_12por
oaire.citationStartPage106por
oaire.citationEndPage114por
oaire.citationVolume11038 LNCSpor
dc.date.updated2021-03-31T19:25:13Z-
dc.identifier.doi10.1007/978-3-030-02628-8_12por
dc.date.embargo10000-01-01-
dc.identifier.eisbn978-3-030-02628-8-
sdum.export.identifier10269-
sdum.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)por
Aparece nas coleções:CMEMS - Artigos em livros de atas/Papers in proceedings

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