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Found 7 papers, 6 papers with code
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
We propose a two-stage MS inflammatory disease activity prediction approach.
GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes
As an example, we generated 100, 000 3D CT volumes, fivefold the number in our real dataset, and trained the classifier exclusively on these synthetic volumes.
Link Prediction for Flow-Driven Spatial Networks
In this work, we focus on link prediction for flow-driven spatial networks, which are embedded in a Euclidean space and relate to physical exchange and transportation processes (e. g., blood flow in vessels or traffic flow in road networks).
ViT-AE++: Improving Vision Transformer Autoencoder for Self-supervised Medical Image Representations
In this paper, we focus on improving ViT-AE (nicknamed ViT-AE++) for a more effective representation of 2D and 3D medical images.
A Domain-specific Perceptual Metric via Contrastive Self-supervised Representation: Applications on Natural and Medical Images
We find that in the natural image domain, CSR behaves on par with the supervised one on several perceptual tests as a metric, and in the medical domain, CSR better quantifies perceptual similarity concerning the experts' ratings.
Differentially Private Graph Classification with GNNs
In this work, we introduce differential privacy for graph-level classification, one of the key applications of machine learning on graphs.
Whole Brain Vessel Graphs: A Dataset and Benchmark for Graph Learning and Neuroscience (VesselGraph)
Moreover, we benchmark numerous state-of-the-art graph learning algorithms on the biologically relevant tasks of vessel prediction and vessel classification using the introduced vessel graph dataset.
