Automated segmentation of dental restorations using deep learning: exploring data augmentation techniques.
Journal:
Oral radiology
PMID:
39652261
Abstract
OBJECTIVES: Deep learning has revolutionized image analysis for dentistry. Automated segmentation of dental radiographs is of great importance towards digital dentistry. The performance of deep learning models heavily relies on the quality and diversity of the training data. Data augmentation is a widely used technique implemented in machine learning and deep learning to artificially increase the size and diversity of a training dataset by applying various transformations to the original data.