Search Results for author:
Found 10 papers, 6 papers with code
Uncertainty in latent representations of variational autoencoders optimized for visual tasks
Deep learning methods are increasingly becoming instrumental as modeling tools in computational neuroscience, employing optimality principles to build bridges between neural responses and perception or behavior.
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.
Domain Generalization via Gradient Surgery
In real-life applications, machine learning models often face scenarios where there is a change in data distribution between training and test domains.
Bridging physiological and perceptual views of autism by means of sampling-based Bayesian inference
Theories for autism spectrum disorder (ASD) have been formulated at different levels: ranging from physiological observations to perceptual and behavioral descriptions.
Learning Deformable Registration of Medical Images with Anatomical Constraints
Deformable image registration is a fundamental problem in the field of medical image analysis.
Blankets Joint Posterior score for learning Markov network structures
Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields.
