Search Results for author:
Found 4 papers, 3 papers with code
Multi-Scale Texture Loss for CT denoising with GANs
To grasp highly complex and non-linear textural relationships in the training process, this work presents a loss function that leverages the intrinsic multi-scale nature of the Gray-Level-Co-occurrence Matrix (GLCM).
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation.
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising
To this end, we can use quantitative image quality assessment scores that we divided into two categories, i. e., paired and unpaired measures.
Multimodal Explainability via Latent Shift applied to COVID-19 stratification
The explanation of the decision taken is computed by applying a latent shift that, simulates a counterfactual prediction revealing the features of each modality that contribute the most to the decision and a quantitative score indicating the modality importance.
