Search Results for author:
Found 8 papers, 2 papers with code
RGI : Regularized Graph Infomax for self-supervised learning on graphs
Self-supervised learning is gaining considerable attention as a solution to avoid the requirement of extensive annotations in representation learning on graphs.
SurvLIMEpy: A Python package implementing SurvLIME
In this paper we present SurvLIMEpy, an open-source Python package that implements the SurvLIME algorithm.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Leishmaniasis Parasite Segmentation and Classification using Deep Learning
Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries.
Cascaded V-Net using ROI masks for brain tumor segmentation
In this work we approach the brain tumor segmentation problem with a cascade of two CNNs inspired in the V-Net architecture \cite{VNet}, reformulating residual connections and making use of ROI masks to constrain the networks to train only on relevant voxels.
3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
This paper analyzes the use of 3D Convolutional Neural Networks for brain tumor segmentation in MR images.
Saliency maps on image hierarchies
In this paper we propose two saliency models for salient object segmentation based on a hierarchical image segmentation, a tree-like structure that represents regions at different scales from the details to the whole image (e. g. gPb-UCM, BPT).
Improving Spatial Codification in Semantic Segmentation
This paper explores novel approaches for improving the spatial codification for the pooling of local descriptors to solve the semantic segmentation problem.
