Researchers propose a conditional quantum flow matching approach for generating synthetic physiological signals when labeled data is scarce. Unlike earlier quantum generative models that begin from uninformative noise, the method incorporates class structure already present in the data. The work targets label-scarce physiological signal classification tasks.
A new arXiv preprint examines using counterfactual image generation to augment training data for medical image analysis. The authors argue that biased datasets produce biased models with limited clinical usefulness, and that synthetic counterfactual images can help offset those biases. The work is announced as a new submission in the cs.LG category.
A new arXiv paper introduces FICAug, a method that combines feature-informed clustering with data augmentation to improve facial-expression-based screening for Parkinson's disease. The approach targets the problem of small clinical datasets, which limits how well such screening models generalize. It is presented as an updated preprint on arXiv (2409.17685v3) in the cs.AI and cs.LG categories.