A semi-supervised, multi-sequence U-Net with late fusion and anatomical priors segments carotid vessel walls and plaques in MRI, with bottleneck fusion outperforming early fusion.
Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation
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abstract
In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentation problem,and we investigate their efficiency in terms of memory and performance.Our experiments, which are performed on BRATS dataset, lead us to the conclusion that learning separate representations for each modality and combining them for brain tumor segmentation could increase the performance of CNN systems.
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Semi-supervised learning and integration of multi-sequence MR-images for carotid vessel wall and plaque segmentation
A semi-supervised, multi-sequence U-Net with late fusion and anatomical priors segments carotid vessel walls and plaques in MRI, with bottleneck fusion outperforming early fusion.