DataAugmentation

MachineLearning

Smarter AI with Less Labeled Data: Unsupervised Data Augmentation

Training AI usually needs lots of human-labeled examples. This work shows a different path: make models learn from plenty of unlabeled data by asking them to stay consistent under strong "noise." Instead of simple tweaks (like small crops or word drops), they use powerful data augmentation: RandAugment for

ultrasound

AI that translates ultrasound videos to fill data gaps

Ultrasound comes in different "flavors" (grayscale and color flow Doppler), but clinical datasets rarely have both for every view. A new study trains a generative AI to "translate" between them—creating realistic videos to fill the gaps. Trained on 54,975 videos and tested on 8,