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Found 6 papers, 5 papers with code
Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRI
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function.
Unsupervised bias discovery in medical image segmentation
It has recently been shown that deep learning models for anatomical segmentation in medical images can exhibit biases against certain sub-populations defined in terms of protected attributes like sex or ethnicity.
CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images
To address this gap, we introduce an extensive chest X-ray multi-center segmentation dataset with uniform and fine-grain anatomical annotations for images coming from five well-known publicly available databases: ChestX-ray8, Chexpert, MIMIC-CXR-JPG, Padchest, and VinDr-CXR, resulting in 657, 566 segmentation masks.
Multi-center anatomical segmentation with heterogeneous labels via landmark-based models
Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but not in the full database.
Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis
To this end, we introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures.
Hybrid graph convolutional neural networks for landmark-based anatomical segmentation
In this work we address the problem of landmark-based segmentation for anatomical structures.
