Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/71252
Título: | Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment |
Autor(es): | Pereira, Sérgio Meier, Raphael Alves, Victor Reyes, Mauricio Silva, Carlos A. |
Data: | 2018 |
Editora: | Springer, Cham |
Revista: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Citação: | Pereira 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_12 |
Resumo(s): | Glioblastoma 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/71252 |
ISBN: | 978-3-030-02627-1 |
e-ISBN: | 978-3-030-02628-8 |
DOI: | 10.1007/978-3-030-02628-8_12 |
ISSN: | 0302-9743 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-3-030-02628-8_12 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
Aparece nas coleções: | CMEMS - Artigos em livros de atas/Papers in proceedings |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Pereira_Meier_Silva@2018.pdf Acesso restrito! | 300 kB | Adobe PDF | Ver/Abrir |