Core Concepts
The author argues that Continual Tuning, through network design and data reuse, efficiently leverages AI predicted and expert revised annotations to enhance interactive segmentation tasks in the medical domain.
Abstract
Interactive segmentation combines AI algorithms with human expertise to improve dataset curation. Continual Tuning addresses issues like catastrophic forgetting and computational inefficiency by freezing shared networks for previous classes and reusing data. The method achieves faster training speeds without compromising performance, demonstrating potential for continual model improvement.
Stats
Continual Tuning achieves a speed 16× greater than training from scratch.
The final average DSC scores can achieve about 76.1% and 78.8% for different backbones.
Using Hybrid Data Continual Tuning improves the mean DSC score of the aorta by 10% compared to Full Training.
The models trained on one dataset show better performance using all 200 CT scans.
Quotes
"Continual Tuning enables AI models to be fine-tuned efficiently (16× faster in our experiment) only with expert revised annotations."
"Our experiments demonstrate that Continual Tuning achieves a speed 16× greater than repeatedly training AI from scratch."