Data Distillation in Medical Imaging Reducing Data Dependency · Part 3 Medical imaging faces persistent data challenges: strict privacy constraints, high annotation costs, limited and imbalanced datasets, and the logistical burden of…
Augmentation and synthetic data now play a central role in medical imaging AI, expanding datasets, improving robustness and enabling new applications such as domain translation, privacy preservation and rare-case simulation.
This article explores label-efficient learning approaches: semi-, unsupervised, and self-supervised learning, that enable AI models to generalize better in data-scarce domains such as medical imaging.
The Data Bottleneck in Medical Imaging Medical imaging AI has achieved remarkable progress, but most breakthroughs still rely on massive, labeled datasets: a luxury often out of reach in healthcare….