publicações selecionadas
-
artigo de conferência
- Segmentation squeeze-and-excitation blocks in stroke lesion outcome prediction 2019
- Towards using memoization for saving energy in android 2019
- Brain Tumour Segmentation based on Extremely Randomized Forest with high-level features. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology SocIETy, EMBS. 2015
- Random decision forests for automatic brain tumor segmentation on multi-modal MRI images 2015
-
artigo de revista
- Combining unsupervised and supervised learning for predicting the final stroke lesion. European Journal of Operational Research. 2021
- Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI with Parametric Maps Using Deep Learning. IEEE Access. 2021
- Prediction of Stroke Lesion at 90-Day Follow-Up by Fusing Raw DSC-MRI with Parametric Maps Using Deep Learning. IEEE Access. 2021
- Adaptive Feature Recombination and Recalibration for Semantic Segmentation With Fully Convolutional Networks. Computational Methods in Applied Sciences. 2019
- Stroke lesion outcome prediction based on MRI imaging combined with clinical information. Frontiers in Neurology. 2018
- Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields. Journal of Neuroscience Methods. 2016
- Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images. European Journal of Operational Research. 2016
-
livro