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Found 11 papers, 3 papers with code
Neural Sign Actors: A diffusion model for 3D sign language production from text
Sign Languages (SL) serve as the primary mode of communication for the Deaf and Hard of Hearing communities.
KGEx: Explaining Knowledge Graph Embeddings via Subgraph Sampling and Knowledge Distillation
To ensure faithfulness, each surrogate is trained by distilling knowledge from the original KGE model.
A Comparative Study of Population-Graph Construction Methods and Graph Neural Networks for Brain Age Regression
We conclude that static graph construction approaches are potentially insufficient for the task of brain age estimation and make recommendations for alternative research directions.
Multimodal brain age estimation using interpretable adaptive population-graph learning
We further show that the assigned attention scores indicate that there are both imaging and non-imaging phenotypes that are informative for brain age estimation and are in agreement with the relevant literature.
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
This makes high-quality GANs useful for unsupervised anomaly detection in medical imaging.
Is MC Dropout Bayesian?
We question the properties of MC Dropout for approximate inference, as in fact MC Dropout changes the Bayesian model; its predictive posterior assigns $0$ probability to the true model on closed-form benchmarks; the multimodality of its predictive posterior is not a property of the true predictive posterior but a design artefact.
Voxel-level Importance Maps for Interpretable Brain Age Estimation
In order to do so, we assume that voxels that are not useful for the regression are resilient to noise addition.
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging.
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
This mismatch is known as sampling bias.
Patch-based Brain Age Estimation from MR Images
Many studies have been proposed for the prediction of chronological age from brain MRI using machine learning and specifically deep learning techniques.
