| Texto completo | |
| Autor(es): |
Carmo, Diedre
;
Rittner, Leticia
;
Lotufo, Roberto
;
Crimi, A
;
Bakas, S
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES (BRAINLES 2020), PT I; v. 12658, p. 11-pg., 2021-01-01. |
| Resumo | |
Segmentation of Glioma from three dimensional magnetic resonance imaging (MRI) is useful for diagnosis and surgical treatment of patients with brain tumor. Manual segmentation is expensive, requiring medical specialists. In the recent years, the Brain Tumor Segmentation Challenge (BraTS) has been calling researchers to submit automated glioma segmentation and survival prediction methods for evaluation and discussion over their public, multimodality MRI dataset, with manual annotations. This work presents an exploration of different solutions to the problem, using 3D UNets and self attention for multitasking both predictions and also training (2D) EfficientDet derived segmentations, with the best results submitted for the official challenge leaderboard. We show that end-to-end multitasking survival and segmentation, in this case, led to better results. (AU) | |
| Processo FAPESP: | 13/07559-3 - Instituto Brasileiro de Neurociência e Neurotecnologia - BRAINN |
| Beneficiário: | Fernando Cendes |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 19/21964-4 - Diagnóstico de doenças pulmonares e COVID a partir de imagens de tomografia computadorizada usando aprendizado profundo explicável |
| Beneficiário: | Diedre Santos do Carmo |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |