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Found 5 papers, 1 papers with code
Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement
Our experiments with two different real-world datasets show that the proposed method (i)~is robust to OOD-noisy test data and provides improved accuracy and (ii)~quantifies voxel-level uncertainty in the predictions.
Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data
Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions.
Single Test Image-Based Automated Machine Learning System for Distinguishing between Trait and Diseased Blood Samples
The novelty of this method comes from distinguishing the trait and the diseased samples from challenging images that have been captured directly in the field.
Robust Super-Resolution GAN, with Manifold-based and Perception Loss
Using loss functions that assume Gaussian-distributed residuals makes the learning sensitive to corruptions in clinical training sets.
Sparse Kernel PCA for Outlier Detection
In this paper, we propose a new method to perform Sparse Kernel Principal Component Analysis (SKPCA) and also mathematically analyze the validity of SKPCA.
