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Found 8 papers, 5 papers with code
Arbitrary Data as Images: Fusion of Patient Data Across Modalities and Irregular Intervals with Vision Transformers
A patient undergoes multiple examinations in each hospital stay, where each provides different facets of the health status.
Multi-Modal Dataset Creation for Federated Learning with DICOM Structured Reports
Methods: DICOM structured reports enable the standardized linkage of arbitrary information beyond the imaging domain and can be used within Python deep learning pipelines with highdicom.
FUNAvg: Federated Uncertainty Weighted Averaging for Datasets with Diverse Labels
Federated learning is one popular paradigm to train a joint model in a distributed, privacy-preserving environment.
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
First, CNNs predict on unlabeled data per label type and then the transformer learns from these predictions with label-specific heads.
Content-Aware Differential Privacy with Conditional Invertible Neural Networks
Manipulation of the latent space leads to a modified image while preserving important details.
Posterior temperature optimized Bayesian models for inverse problems in medical imaging
In POTOBIM, we optimize both the parameters of the prior distribution and the posterior temperature with respect to reconstruction accuracy using Bayesian optimization with Gaussian process regression.
Posterior Temperature Optimization in Variational Inference for Inverse Problems
Bayesian methods feature useful properties for solving inverse problems, such as tomographic reconstruction.
Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior
We use a randomly initialized convolutional network as parameterization of the reconstructed image and perform gradient descent to match the observation, which is known as deep image prior.
