An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks …
Uncertainty-based graph convolutional networks for organ segmentation refinement
Organ segmentation is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, CNNs have dominated the state of the art in …
Self-attention equipped graph convolutions for disease prediction
Multi-modal data comprising imaging (MRI, fMRI, PET, etc.) and non-imaging (clinical test, demographics, etc.) data can be collected together and used for disease prediction. Such …
InceptionGCN: receptive field aware graph convolutional network for disease prediction
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks …
Graph Convolution Based Attention Model for Personalized Disease Prediction
Clinicians implicitly incorporate the complementarity of multi-modal data for disease diagnosis. Often a varied order of importance for this heterogeneous data is considered for …