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Found 7 papers, 5 papers with code
Multi-stage Deep Layer Aggregation for Brain Tumor Segmentation
Gliomas are among the most aggressive and deadly brain tumors.
Combining unsupervised and supervised learning for predicting the final stroke lesion
Predicting the final ischaemic stroke lesion provides crucial information regarding the volume of salvageable hypoperfused tissue, which helps physicians in the difficult decision-making process of treatment planning and intervention.
Adaptive feature recombination and recalibration for semantic segmentation with Fully Convolutional Networks
In this paper, we propose recombination of features and a spatially adaptive recalibration block that is adapted for semantic segmentation with Fully Convolutional Networks - the SegSE block.
Retinal vessel segmentation based on Fully Convolutional Neural Networks
The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them.
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
In this way, we overcome the need for expert annotations of regions of interest.
Enhancing clinical MRI Perfusion maps with data-driven maps of complementary nature for lesion outcome prediction
Stroke is the second most common cause of death in developed countries, where rapid clinical intervention can have a major impact on a patient's life.
Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI
However, this is not optimal in FCN due to the spatial correspondence between units and voxels.
